I. Executive Summary
In recognizing the impacts of climate change and monitoring the environment to achieve Sustainable Development Goals collaborative solutions are required. This calls for standardized data and tools. But how can we ensure an effective exchange of reliable information across disciplines without sacrificing individual users’ needs? Climate services require vast volumes of data from different providers to be processed by various scientific ecosystems: Raw data needs to be transformed into analysis ready data and from there into indicators to become more useful for supporting decisions. To provide a processing infrastructure that supports collaboration, we need standards based on the principles of being findable, accessible, interoperable, and reusable. OGC standards are aligned with these principles allowing for the reuse of data refinement features across political boundaries, organizations, and administrative levels.
Policy instruments should include technological guidelines, for example mandate the use of international standards for data formats, metadata and machine to machine communication protocols, in order to foster interoperability and software reuse. This would strengthen international collaboration on software development, and in turn contribute to the deployment of effective, robust and scientifically credible climate resilience information systems. With increased data access and interoperability of data, processing tools and data infrastructure, it can reduce human and economic costs and ensure appropriate policies are implemented to benefit all.
The OGC Climate Resilience Pilot, as the initial phase of a series of long-term climate initiatives, aimed to transform geospatial data, technologies, and other capabilities into meaningful information for various stakeholders, including decision makers, scientists, policy makers, data providers, software developers, and service providers. The pilot demonstrated the establishment of data pipelines to convert vast amounts of raw data through various steps into decision-ready information. To get to decision ready information the data first needs to be organised from multiple sources into processing pipelines, towards analysis-ready formats. The importance of analysis-ready data and decision-ready indicators was emphasized through discussions on various aspects of GEODataCubes. The pilot explored scientific aspects of climate impact by examining case studies related to droughts, floods, and wildfires, highlighting assessment tools and the complexities of climate indices. Ultimately, this Climate Resilience Pilot serves as a valuable resource for making informed decisions to support and enhance climate action. It specifically assists the location community in developing powerful visualization and communication tools to effectively address ongoing climate threats such as heat, drought, floods, and wildfires.
One of the biggest gaps to date has been the challenge of translating the outputs of global climate models into specific impacts and risks at the local level. The climate modeling community has embraced standards and there is a wide array of data for modelers to exchange and compare, with numerous climate data services now available online. However, outside the weather and climate domain, planners and GIS analysts working for agencies responsible for climate change impacts have limited familiarity with and capacity to consume climate model results. Because of this, a key focus of this pilot was exploring methods for extracting climate variables from climate model output scenarios and transforming them into a form more readily consumable via GIS platforms and applied to the local level. Climate variables relevant to use case impacts were selected. Climate variable data cubes were extracted into temporal and spatial ranges specific to the use cases. Finally the data structure was transformed from multidimensional grided cubes into data forms more readily consumable by geospatial applications. For example open standards were employed such as 2D OGC geopackage and geojson point data and published to OGC API services — making them readily available and explorable by a much wider user community. These pilot data flows serve as useful examples of how climate model results can be translated into impacts and risk estimates at the local level in a way that is easy to integrate into existing planning workflows.
With the target user group of non-technical decision makers, the workflow from data to visualisation is being shown at several chapters of this report. A dedicated chapter is pointing out the options and challenges of artificial intelligence usage to establish a 5D meta world where the efficiency of climate action can be simulated. Reduction of disaster risks due to technical engineering constructions like dams are able to be simulated. Climate resilience is not only an aspect of shift of meteorological phenomena but also related to land degradation and loss of biodiversity. For this pilot, the effects of climate change were explored in the context of vegetation in the LA area. Options for 3D landscape vegetation simulation are shown, including how well various species survive under changing climate conditions represented by a range of climate and policy scenarios over time.
The pilot recognizes the significant challenges associated with effectively conveying information to decision-makers, which necessitates a thorough examination of communication methods. As a result, a dedicated chapter has been incorporated into the pilot’s work to address this issue. This chapter emphasizes unique approaches that facilitate effective communication with non-technical individuals who frequently hold responsibility for local climate resilience action strategies. By focusing on communication, the pilot aims to bridge the gap between technical and non-technical stakeholders, ensuring that vital information is conveyed accurately and comprehensively. The inclusion of this chapter serves as a testament to the pilot’s aim to enhancing communication strategies for improved decision-making in the realm of climate resilience.
II. Keywords
The following are keywords to be used by search engines and document catalogues.
Climate Resilience, data, ARD, component, use case, FAIR, Drought, Heat, Fire, Floods, Data cubes, Climate scenario, Impact, Risk, Hazard, DRI, Indicator
III. Security considerations
No security considerations have been made for this document.
IV. Submitters
The various organizations and institutes that contribute to the Climate Resilience Pilot are described below.
| Name | Organization | Role or Summary of contribution |
|---|---|---|
| Guy Schumann | RSS-Hydro | Lead ER Editor |
| Albert Kettner | RSS-Hydro/DFO | Lead ER Editor |
| Timm Dapper | Laubwerk GmbH | |
| Peng Yue | Wuhan University | Datacube component |
| Zhe Fang | Wuhan University | Climate ARD component |
| Hanwen Xu | Wuhan University | Drought impact use cases |
| Dean Hintz | Safe Software, Inc. | |
| Samantha Lavender | Pixalytics Ltd | Development of drought indicator |
| Andrew Lavender | Pixalytics Ltd | Development of drought indicator |
| Jenny Cocks | Pixalytics Ltd | Development of drought indicator |
| Jakub P. Walawender | Freelance climate scientist and EO/GIS expert | Solar radiation use case |
| Kailin Opaleychuk | Safe Software, Inc. | |
| Daniela Hohenwallner-Ries | alpS GmbH | Communication with stakeholders |
| Hanna Krimm | alpS GmbH | Communication with stakeholders |
| Hinnerk Ries | alpS GmbH | Communication with stakeholders |
| Paul Schattan | alpS GmbH | Communication with stakeholders |
| Jérôme Jacovella-St-Louis | Ecere Corporation | Datacube API client and server |
| Patrick Dion | Ecere Corporation | Datacube API client and server |
| Eugene Yu | GMU | |
| Gil Heo | GMU | |
| Glenn Laughlin | Pelagis Data Solutions | Coastal Resilience & Climate Adaptation |
| Tom Landry | Intact Financial Corporation | |
| Nils Hempelmann | OGC | Climate resilience Pilot Coordinator |
IV.A. About alpS
The alpS GmbH is an international engineering and consulting firm that supports companies, municipalities, and governments in their sustainable development and in dealing with the consequences, opportunities, and risks of climate change. Over the past 20 years, alpS has worked with more than 250 municipalities and industrial partners on climate-related projects. alpS accompanied a large number of adaptation cycles from risk assessments to the implementation of adaptation measures and their evaluation.
IV.B. About Ecere
Ecere is a small software company located in Gatineau, Québec, Canada. Ecere develops the GNOSIS cross-platform suite of geospatial software, including a map server, a Software Development Kit and a 3D visualization client. Ecere also develops the Free and Open Source Ecere cross-platform Software Development Kit, including a 2D/3D graphics engine, a GUI toolkit, an Integrated Development Environment and a compiler for the eC programming language. As a member of the OGC, Ecere is an active contributor in several Standard Working Groups as co-chair and editor, and participated in several testbeds, pilots and code sprints. In particular, Ecere has been a regular contributor and an early implementer for several OGC API standards in its GNOSIS Map Server and GNOSIS Cartographer client, and is also active in the efforts to modernize the OGC CDB data store and OGC Styles & Symbology standard.
IV.C. About George Mason University (GMU)
George Mason University (GMU) is a public research university that conducts research and provides training to postdoctoral fellows, PhD candidates, and master’s students in Geospatial information science, remote sensing, satellite image analysis, geospatial data processing, Earth system science, geospatial interoperability and standards, geographic information systems, and other related subjects. GMU will contribute an ARD use-case.
IV.D. About Intact
Intact Financial Corporation (IFC) is the largest provider of Property & Casualty (P&C) insurance in Canada. Its purpose is to help people, businesses and society prosper in good times and be resilient in bad times1. The company has been on the front lines of climate change with our customers for more than a decade – getting them back on track and helping them adapt. As extreme weather is going to get worse over the next decade, Intact intends to double down on adapting to this changing environment and be better prepared for floods, wildfire and extreme heat2.
With close to 500 experts in data, artificial intelligence, machine learning and pricing, the Intact Data Lab has deployed almost 300 AI models in production to-date. It is focused on improving risk selection and making operations as efficient as possible while creating outstanding interactions with customers. Within Intact’s Data Lab, the Centre for Climate and Geospatial Analytics (CCGA) uses weather, climate, and geospatial data along with machine learning models and claims data to develop risk maps and other specialized products to the business.
IV.E. About Laubwerk
Laubwerk is a software development company whose mission is to combine accurate, broadly applicable visualizations of vegetation with deeper information and utility that goes far beyond their visual appearance. We achieve this through building a database that combines ultra-realisting 3D representation of plants with extensive metadata that represents plant properties. This unique combination makes Laubwerk a prime partner to bridge the gap from data-driven simulation to eye-catching visualizations.
IV.F. About Pixalytics Ltd
Pixalytics Ltd is an independent consultancy company specializing in Earth Observation (EO). We combine cutting-edge scientific knowledge with satellite and airborne data to provide answers to questions about our planet’s resources and behavior. The underlying work includes developing algorithms and software, with activities including a focus on EO quality control and end-user focused applications.
IV.G. About Pelagis
Pelagis is an OceanTech venture located in Nova Scotia, Canada. Our foundation focuses on the application of open geospatial technology and standards designed to promote the sustainable use of our ocean resources. As a member of the Open Geospatial Consortium, we co-chair the Marine Domain Working Group responsible for developing a spatially-aware federated service model of marine and coastal ecosystems.
IV.H. About Presagis
ToDo: Description
IV.I. About RSS-Hydro
RSS-Hydro is a geospatial solutions and service company focusing its R&D and commercial products in the area of water risks, with a particular emphasis on the SDGs. RSS-Hydro has been part of several successful OGC testbeds, including the DP 21 to which this pilot is linked, not only in terms of ARD and IRD but also in terms of use cases. In this pilot, RSS-Hydro’s main contribution is the lead of the Engineering report. In terms of technical contributions to various other OGC testbeds and pilots, RSS-Hydro is creating digestible OGC data types and formats for specific partner use cases, in particular producing ARD from publically available EO and model data, including hydrological model output as well as climate projections. These ARD will feed into all use cases for all participants, especially use cases proposed for Floods, Heat, Drought and Health Impacts by other participants in the pilot. The created ARD in various OGC interoperable formats will create digestible dataflows for the proposed OGC Use Cases.
Specifically, RSS-Hydro can provide access to the following satellite and climate projection data:
Wildfire: Fire Radiant Power (FRP) product from Sentinel 3 (NetCDF), 5p, MODIS products (fire detection), VIIRS (NOAA); possibly biomass availability (fire fuel).
Land Surface Temp: Sentinel 3
Pollution: Sentinel 5p
Climate Projection data (NetCDF, etc., daily downscaled possible): air temp (10 m above ground). Rainfall and possibly wind direction as well
Satellite-derived Discharge Data to look at Droughts/Floods etc. by basin or other scale.
Hydrological model simulation outputs at (sub)basin scale.
IV.J. About Safe Software
Safe Software is a leader in supporting geospatial interoperability and automation for more than 25 years as creators of the FME platform. FME was created to promote FAIR principles, including data sharing across barriers and silos, with unparalleled support for a wide array of both vendor specific formats and open standards. Within this platform, Safe Software provides a range of tools to support interoperability workflows. FME Form is a graphical authoring environment that allows users to rapidly prototype transformation workflows in a no-code environment. FME Flow then allows users to publish data transforms to enterprise oriented service architectures. FME Hosted offers a low cost, easy to deploy and scalable environment for deploying transformation and integration services to the cloud.
Open standards have always been a core strategy for Safe Software to better support data sharing. The FME platform can be seen as a bridge between the many supported vendor protocols and open standards such as XML, JSON and OGC standards such as GML, KML, WMS, WFS and OGC APIs. Safe has collaborated extensively over the years with the open standards community. Safe actively participates in the CityGML and INSPIRE communities in Europe. We are also active within the OGC community and participated in many initiatives including test beds, pilots such as Maritime Limits and Boundaries and IndoorGML, and most recently the 2021 Disaster Pilot and 2023 Climate Resilience Pilot. Safe also actively participates in a number of Domain and Standards working groups.
IV.K. About Jakub P. Walawender
Jakub P. Walawender is a freelance climate scientist and EO/GIS expert who carries out his PhD research on solar radiation climatology of his home country of Poland at the Laboratory for Climatology and Remote Sensing (LCRS), Faculty of Geography, Philipps University in Marburg, Germany. Jakub specialises in the application of satellite remote sensing, GIS and geostatistics in the monitoring and analysis of climate variability and extremes. He also supports users in the application of different climate data records to tackle the effects of climate change.
IV.L. About Wuhan University (WHU)
Wuhan University (WHU) is a university that plays a significant role in researching and teaching all aspects of surveying and mapping, remote sensing, photogrammetry, and geospatial information sciences in China. In this Climate Resilience Pilot, WHU will contribute three components (ARD, Drought Indicator, and Data Cube) and one use-case (Drought Impact Use-cases).
Engineering report for OGC Climate Resilience Pilot
1. Terms, definitions and abbreviated terms
No terms and definitions are listed in this document.
Data Cube
In computer programming contexts, a data cube (or datacube) is a multi-dimensional (“n-D”) array of values. Typically, the term data cube is applied in contexts where these arrays are massively larger than the hosting computer’s main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data.
FAIR Climate Service
Climate resilience information system where the entire architecture is following FAIR principles.
FAIR principle
Findability, Accessibility, Interoperability, and Reuse of digital assets.
Resilience
Ability of a system to compensate impacts.
Sentinel (satellite mission)
A series of next-generation Earth observation missions developed by the European Space Agency (ESA), on behalf of the joint ESA/European Commission initiative Copernicus.
1.1. Abbreviated terms
ADES
Application Deployment and Execution Service
AP
Application Package
API
Application Programming Interface
ARD
Analysis Ready Data
ARDC
Analysis Ready Data Cube
Carrying Capacity
An area both suitable and available for human activity based on the state of the ecosystem and competitive pressures for shared resources
CDI
Combined Drought Indicator
CDR
Climate Data Record
CEOS
Committee on Earth Observation Satellites
CRIS
Climate Resilience Information System
CRMA
Climate Mapping for Resilience and Adaptation
ECV
Essential Climate Variable
EMS
Exploitation Platform Management Service
EO
Earth Observation
ESA
European Space Agency
EUMETSAT
European Organisation for the Exploitation of Meteorological Satellites
FME
Feature Manipulation Engine
GCOS
Global Climate Observing System
GOOS
Global Ocean Observing System
OMSv3
OGC Observations & Measurements 3.0
RCI
Regional Climate Indicator
SDG
Sustainable Development Goal
SMA
Soil Moisture Anomaly
SPI
Standardized Precipitation Index
S3
Simple Storage Service
UNFCCC
United Nations Framework Convention on Climate Change
WG Climate
Joint Working Group on Climate
WPS
Web Processing Service
2. Introduction
2.1. Enhancing Interoperability for Climate Resilience Information Systems
The OGC Climate Resilience Pilot represents the first phase of multiple long term climate activities aiming to evolve geospatial data, technologies, and other capabilities into valuable information for decision makers, scientists, policy makers, data providers, software developers, and service providers so we can make valuable, informed decisions to improve climate action. The goal was to help the location community develop more powerful visualization and communication tools to accurately address ongoing climate threats such as heat, drought, floods, fires as well as supporting the national determined contributions for greenhouse gas emission reduction. Climate resilience is often considered the use case of our lifetime, and the OGC community is uniquely positioned to accelerate solutions through collective problem solving with this initiative.
Figure 1
As illustrated, big, raw data from multiple sources requires further processing in order to be ready for analysis and climate change impact assessments. Applying data enhancement steps, such as bias adjustments, re-gridding, or calculation of climate indicators and essential variables, leads to “Decision Ready Indicators.” The spatial data infrastructures required for this integration should be designed with interoperable building blocks following FAIR data principles. Heterogeneous data from multiple sources can be enhanced, adjusted, refined, or quality controlled to provide Science Services data products for Climate Resilience. The OGC Climate Change Services Pilots also illustrated the graphical exploration of the Decision Ready Climate Data. It effectively demonstrated how to design FAIR climate services information systems. The OGC Pilot participants illustrated the necessary tools and the visualizations to address climate actions moving towards climate resilience.
2.2. The Role of the Pilot
The goal of this pilot was to enable everyone to take the relevant actions to address climate change and make well informed decisions for climate change adaptation. This includes scientists, decision makers, city managers, as well as politicians. So what do we need? We need data from lots of organizations, available at different scales for large and small areas to be integrated with scientific processes, analytical models, and simulation environments. We need data visualization and communication tools to shape the message in the right way for any client. Many challenges can be met through resources that adhere to FAIR principles. FAIR as in: Findable, Accessible, Interoperable, and Reusable. No single organization has all the data we need to understand the consequences of climate change. The OGC Climate Resilience Community identifies, discusses, and develops these resources.
The OGC Climate Resilience Community has a vision to support efforts on climate actions and enable international partnerships (SDG 17), and move towards global interoperable open digital infrastructures providing climate resilience information on users demand. This pilot contributed to establishing an OGC climate resilience concept store for the community where all appropriate climate information to build climate resilience information systems as open infrastructures can be found in one place, be it Information about data services, tools, software, handbooks, or a place to discuss experiences and needs. It covers all phases of Climate Resilience, from initial hazards identification and mapping to vulnerability and risk analysis to options assessments, prioritization, and planning, and ends with implementation planning and monitoring capabilities. These major challenges can only be met through the combined efforts of many OGC members across government, industry, and academia.
This Pilot has set the stage for a series of follow up activities. It therefore focused on use-case development, implementation, and exploration. It also answered the questions such as:
What use-cases can be realized with the current data, services, analytical functions, and visualization capabilities that we have? Current data services include for example the Copernicus Services, including Climate Data Store (CDS) https://cds.climate.copernicus.eu/ and Atmosphere Data Store (ADS) https://ads.atmosphere.copernicus.eu/.
How much effort is it to realize these use-cases?
What is missing, or needs to be improved, in order to transfer the use-cases developed in the pilot to other areas?
2.3. Objectives
The pilot had three objectives. First, to better understand what is currently possible with the available data and technology. Second, what additional data and technology needs to be developed in future to better meet the needs of the Climate Resilience Community; and third, to capture Best Practices, and to allow the Climate Community to copy and transform as many use-cases as possible to other locations or framework conditions.
2.4. Background
With growing local communities, an increase in climate-driven disasters, and an increasing risk of future natural hazards, the demand for National Resilience Frameworks and Climate Resilience Information Systems (CRIS) cannot be overstated. Climate Resilience Information Systems (CRIS) are enabling data-search, -fetch, -fusion, -processing and -visualization. They enable access, understanding, and use of federal data, facilitate integration of federal and state data with local data, and serve as local information hubs for climate resilience knowledge sharing.
CRIS are already existing and operational, like the Copernicus Climate Change Service with the Climate Data Store. CRIS architectures can be further enhanced by providing climate scientific methods and visualization capabilities as climate building blocks. Based on FAIR principles, these building blocks enable in particular the reusability of Climate Resilience Information Systems features and capabilities. Reusability is an essential component when goals, expertises, and resources are aligned from the national to the local level. Framework conditions differ across the country, but building blocks enable as much reuse of existing Best Practices, tools, data, and services as possible.
Goals and objectives of decision makers vary at different scales. At the municipal level, municipal leaders and citizens directly face climate-related hazards. Aspects thus come into focus such as reducing vulnerability and risk, building resilience through local measures, or enhancing emergency response. At the state level, the municipal efforts can be coordinated and supported by providing funding and enacting relevant policies. The national, federal, or international level provides funding, science data, and international coordination to enable the best analysis and decisions at the lower scales.
Figure 2
Productivity and decision making are enhanced when climate building blocks are exchangeable across countries, organizations, or administrative levels (see Figure below). This OGC Climate Resilience Pilot is a contribution towards an open, multi-level infrastructure that integrates data spaces, open science, and local-to-international requirements and objectives. It contributes to the technology and governance stack that enables the integration of data including historical observations, real time sensing data, reanalyses, forecasts or future projections. It addresses data-to-decision pipelines, data analysis and representation, and bundles everything in climate resilience building blocks. These building blocks are complemented by Best Practices, guidelines, and cook-books that enable multi–stakeholder decision making for the good of society in a changing natural environment.
The OGC Innovation Program brings all groups together: The various members of the stakeholder group define use cases and requirements, the technologists and data providers experiment with new tools and data products in an agile development process. The scientific community provides results in appropriate formats and enables open science by providing applications that can be parameterized and executed on demand.
Figure 3
This OGC Climate Resilience Pilot is part of the OGC Climate Community Collaborative Solution and Innovation process, an open community process that uses the OGC as the governing body for collaborative activities among all members. A spiral approach is applied to connect technology enhancements, new data products, and scientific research with community needs and framework conditions at different scales. The spiral approach defines real world use cases, identifies gaps, produces new technology and data, and tests these against the real world use cases before entering the next iteration. Evaluation and validation cycles alternate and continuously define new work tasks. These tasks include documentation and toolbox descriptions on the consumer side, and data and service offerings, interoperability, and system architecture developments on the producer side. It is emphasized that research and development is not constrained to the data provider or infrastructure side. Many tasks need to be executed on the data consumer side in parallel and then merged with advancements on the provider side in regular intervals.
Good experiences have been made using OGC API standards in the past. For example, the remote operations on climate simulations (roocs) use OGC API Processes for subsetting data sets to reduce the data volume being transported. Other systems use OGC STAC for metadata and data handling or OGC Earth Observation Exploitation Platform Best Practices for the deployment of climate building blocks or applications into CRIS architectures. Still data handling regarding higher complex climate impact assessments within FAIR and open infrastructures needs to be enhanced. There is no international recommendation or best practice on usage of existing API standards within individual CRIS. It is the goal of this pilot to contribute to the development of such a recommendation, respecting existing operational CRIS that are serving heterogen user groups
Figure 4
2.5. Technical Challenges
Realizing the delivery of Decision Ready Data on demand to achieve Climate Resilience involves a number of technical challenges that have already been identified by the community. A subset will be selected and embedded in use-cases that will be defined jointly by Pilot Sponsors and the OGC team. The goal is to ensure a clear value-enhancement pipeline as illustrated in Figure 1, above. This includes, among other elements, a baseline of standardised operators for data reduction and analytics. These need to fit into an overall workflow that provides translation services between upstream model data and downstream output — basically from raw data, to analysis-ready data, to decision-ready data. The following technical challenges have been identified and will be treated in the focus areas cycles of the Pilot accordingly:
Big Data Challenge: Multiple obstacles still exist, creating big barriers for seamless information delivery starting from Data Discovery. Here the emergence of new data platforms, new processing functionalities, and thus new products, data discovery remains a challenge. In addition to existing solutions based on established metadata profiles and catalog services, new technologies such as OGC’s Spatio-Temporal Asset Catalog (STAC) and open Web APIs such as OGC API Records will be explored. Furthermore, aspects of Data Access need to be solved where the new OGC API suite of Web APIs for data access, subsetting, and processing are currently utilized very successfully in several domains. Several code sprints have shown that server-side solutions can be realized within days and clients can interact very quickly with these server endpoints, thus development time is radically reduced. A promising specialized candidate for climate data and non-climate data integration has been recently published in the form of the OGC API — Environmental Data Retrieval (EDR). But which additional APIs are needed for climate data? Is the current set of OGC APIs sufficiently qualified to support the data enhancement pipeline illustrated in Figure 1? If not, what modifications and extensions need to be made available? How do OGC APIs cooperate with existing technologies such as THREDDS and OPEnDAP? For challenges of data spaces, Data Cubes have recently been explored in the OGC data cube workshop. Ad hoc creation and embedded processing functions have been identified as essential ingredients for efficient data exploration and exchange. Is it possible to transfer these concepts to all stages of the processing pipeline? How to scale both ways from local, ad hoc cubes to pan-continental cubes and vice versa. How to extend cubes as part of data fusion and data integration processes?
Cross-Discipline Data Integration: Different disciplines such as Earth Observation, various social science, or climate modeling use different conceptual models in their data collection, production, and analytical processes. How can we map between these different models? What patterns have been used to transform conceptual models to logical models, and eventually physical models? The production of modern Decision-ready information needs the integration of several data sets, including census and demographics, further social science data, transportation infrastructure, hydrography, land use, topography and other data sets. This pilot cycle uses ‘location’ as the common denominator between these diverse data sets and works with several data providers and scientific disciplines. In terms of Data Exchange Formats the challenge is to know what data formats need to be supported at the various interfaces of the processing pipeline? What is the minimum constellation of required formats to cover the majority of use cases? What role do container formats play? Challenging on technical level is also the Data Provenance. Many archives include data from several production cycles, such as IPCC AR 5 and AR 6 models. In this context, long term support needs to be realized and full traceability from high level data products back to the original raw data. Especially in context of reliable data based policy, clear audit trails and accountability for the data to information evolution needs to be ensured.
Building Blocks for processing pipelines: With a focus on Machine Learning and Artificial Intelligence which plays an increasing role in the context of data science and data integration. This focus area needs to evaluate the applicability of machine learning models in the context of the value-enhancing processing pipeline. What information needs to be provided to describe machine learning models and corresponding training data sufficiently to ensure proper usage at various steps of the pipeline? Upcoming options to deploy ML/AI within processing APIs to enhance climate services are rising challenges e.g. on how to initiate or ingest training models and the appropriate learning extensions for the production phase of ML/AI. Heterogeneity in data spaces can be bridged with Linked Data and Data Semantics. Proper and common use of shared semantics is essential to guarantee solid value-enhancement processes. At the same time, resolvable links to procedures, sampling & data process protocols, and used applications will ensure transparency and traceability of decisions and actions based on data products. What level is currently supported? What infrastructure is required to support shared semantics? What governance mechanisms need to be put in place?
2.6. How is this Pilot Relevant to the Climate Resilience Domain Working Group?
The Climate Resilience DWG will concern itself with technology and technology policy issues, focusing on geospatial information and technology interests as related to climate mitigation and adaptation as well as the means by which those issues can be appropriately factored into the OGC standards development process.
The mission of the Climate Resilience DWG is to identify geospatial interoperability issues and challenges that impede climate action, then examine ways in which those challenges can be met through application of existing OGC Standards, or through development of new geospatial interoperability standards under the auspices of OGC.
Activities to be undertaken by the Climate Resilience DWG include, but are not limited to:
Identify the OGC interface standards and encodings useful to apply FAIR concepts to climate change services platforms;
Liaise with other OGC Working Groups (WGs) to drive standards evolution;
Promote the usage of the aforementioned standards with climate change service providers and policy makers addressing international regional and local needs;
Liaise with external groups working on technologies relevant to establishing ecosystems of EO Exploitation Platforms;
Liaise with external groups working on relevant technologies;
Publish OGC Technical Papers, Discussion Papers or Best Practices on interoperable interfaces for climate change services;
Provide software toolkits to facilitate the deployment of climate change services platforms.
2.7. Component workflow
Interoperability plays a vital role in facilitating climate resilience by enabling seamless integration and exchange of information between data, models, and various components. During this pilot, participanta have worked on a number of workflows and architectures focusing on several use cases of droughts, heatwaves, and fires. Generally speaking, such a workflow or process takes the following form and tasks.
The different component workflows of this pilot considered the process of processing raw data either directly to Analysis Ready Data (ARD) or moving big (raw) data first to data cubes for efficient handling and then processing them to ARD. Subsequently, participants illustrated how to tranform ARD into decision ready information (DRI) and climate indicators. The pilot also demonstrated the value-added of high-end 3D visualization for increasing climate resilience and for facilitating the decision-making process.
By following the above workflow process, the interoperability of data, models, and components is maximized, facilitating a comprehensive understanding of droughts, heatwaves, and fires and supporting informed decision-making for climate resilience strategies.
3. Raw data to Datacubes
Raw data and Datacubes are two different forms of organizing and structuring data in the context of data analysis and data warehousing.
Raw Data refers to the unprocessed, unorganized, and unstructured data that is collected or generated directly from various sources. It can include a variety of forms such as text, numbers, (geo) images, audio, video, or any other form of data. Raw data often lacks formatting or context and requires further processing or manipulation before it can be effectively analyzed or used for decision-making purposes. Raw data is typically stored in databases or data storage systems.
Datacubes, also known as multidimensional cubes are a structured form of data representation that organizes and aggregates raw data into a multi-dimensional format. Datacubes are designed to facilitate efficient and fast analysis of data from different dimensions or perspectives. They are commonly used in data warehousing.
Datacubes organize data into a multi-dimensional structure typically comprising dimensions, hierarchies, and cells. Dimensions represent various attributes or factors that define the data, such as time, geography, or products. Hierarchies represent the levels of detail within each dimension. Cells typically store the aggregated data values at the intersection of dimensions.
Datacubes enable users to perform complex analytical operations like slicing, dicing, drilling down, or rolling up data across different dimensions. They provide a summarized and pre-aggregated view of data that can significantly speed up query processing and analysis compared to working directly with raw data, something that is very valuable for the climate resilience community. Therefore, Datacubes are often used to support decision-making processes. The example below highlights a climate resilience related example of how to create and make available Datacubes for wildfire risk analysis.
3.1. Analysis Ready Data Cubes — user-friendly sharing of climate data records
Climate Data Record (CDR) is a time serie of measurements of sufficient length, consistency, and continuity to determine potential climate variability and change (US National Research Council). These measurements can be obtained through groundbased stations or derived from a long time series of satellite data.
Data Cube (Different approaches): Datacubes organize data into a multi-dimensional structure. They contain typically:
multidimensional arrays of data (Kopp et al., 2019);
4-dimensional array with dimensions x (longitude or easting), y (latitude or northing), time, and bands sharing the same data properties (Appel and Pebesma, 2019)
where “cube” can be a metaphor to help illustrate a data structure that can in fact be 1- dimensional, 2-dimensional, 3-dimensional, or higher-dimensional. The dimensions may be coordinates or enumerations, e.g., categories (OGC, 2021)
Common (technical) definition of the Data Cube focuses on the data structure aspects exclusively!
Analysis Ready Data (ARD) — are data sets that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets. ARDs are often representing satellite data (CEOS website).
The idea behind the ARD is, that data providers such as e.g. EUMETSAT or ESA are better suited to do data pre-processing, e.g. atmospheric correction, cloud masking and re-gridding, than users.
Analysis Ready Data Cubes (ARDCs)
ARDs are often made available in the form of data cubes, which focus on a one specific region (e.g. Swiss Data Cube) or thematic application (e.g. EUMETSAT Drought & Vegetation Data Cube (D&V Data Cube)). Data cubes can also be defined by the type of data it includes (e.g. Atmospheric Composition Data Cube (ACDC), Earth System Data Cube (ESDC) + Data Analytics Toolkit (Earth System Data Lab)).
A data cube which contains various climate data records can be generally referred to as Climate Data Cube.
Table 1 — Example Climate Data Cubes
| Data Cube name | Climate Data Records | Provider | Year of release | Data source | Accessibility | Data format | Temporal coverage |
|---|---|---|---|---|---|---|---|
| EUMETSAT Drought & Vegetation Data Cube | Solar radiation: Global Radiation, Direct normal Solar Radiation, Sunshine Duration, other: Land Surface Temperature, Reference Evapotranspiration, NDVI, Fractional Vegetation Cover, Leaf Area Index, Fraction of absorbed photosynthetically active radiation, Soil Wetness Index (root zone), Precipitation, Air temperature at 2m | EUMETSAT | 2021 | CMSAF SARAH2 (for solar radiation), other: LSA SAF, H SAF, GPCC, ECMWF | Free after enrollment (EUMETSAT Prototype Satellite Data Cube) | CF compliant netCDF4 via a THREDDS server | Solar radiation: 1983-2020, other: 2004-2020, SWI 1992-2020, Precipitation 1982-2020, T2m 1979-2020 |
| Solar radiation: Day, month, other: hourly (LST), 10-daily (NDVI) | 0.05° x 0.05° degrees | Europe | mesogeos — a Daily Datacube for the Modeling and Analysis of Wildfires in the Mediterranean | Solar radiation: mean daily surface solar radiation downwards from ERA5-Land, other: dynamic variables — previous day Leaf Area Index, evapotransiration, Land Surface Temperature, meteorological data, fire variables and Fire Weather Index, static variables — roads density, population density and topography layers | One of many data cubes created within the Deep Cube (Horizon 2020 Project “Explainable AI pipelines for big Copernicus data”") | 2022 | MODIS, ERA5, JRC European Drought Observatory, worldpop.org, Copernicus C3S, Copernicus EU-DEM, EFFIS |
| Free, with open code at github | .zarr (file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification), Jupyter Notebooks (python) | 2002 — 2022 | Day | 1km x 1km | Mediterranean:Lon: 10.72 W, to 30.07 E, Lat: 36.74 to 47.7 N | The Earth System Data Cube (ESDC) | Solar radiation: Surface Net Solar Radiation, other: the cube includes all important meteorological variables (the list is too long to include in this table) |
| DeepESDL Team (ESA-funded project Earth System Data Lab) | 2022 | ERA5 (for solar radiation) | Free | Directory of NetCDF files based on xcube, can also be accessed via a dedicated ESDL THREDDS server which supports the OPeNDAP and WCS | 1979 — 2021 | 1h | 0.25 degree |
| Global | MADIA — Meteorological variables for agriculture (Italy) | Solar radiation: mean of daily surface solar radiation downwards (shortwave radiation), other 10-day gridded agro-meteorological data: air temperature and humidity, precipitation, wind speed, evapotranspiration | Council for Agricultural Research and Economics–Research Centre for Agriculture and Environment | 2022 | ERA5 hourly data accessed through Climate Data Store | Free | NetCDF, csv and vector file (Shapefile) for administrative regions (NUTS 2 and 3) |
| 1981 — 2021 | 10-day, also climate normals, minimum, maximum and the main quantiles | 0.25 degree | Italy: 34.875–48.125 N, 4.875 – 20.125 E | Open Environmental Data Cube | Climate: air temperature (Min, Mean, Max), land surface temperature (Min, Mean, Max), precipitation (Daily Sum), other: natural disasters, air quality, land cover, terrain, soil, forest and vegetation | OpenGeoHub, CVUT Prague, mundialis,Terrasigna, MultiOne (Horizon2020 Project: “Geo-harmonizer: EU-wide automated mapping system for harmonization of Open Data based on FOSS4G and Machine”) | 2022 |
Analysis Ready Data Cubes (ARDCs) play an important role in handling large volumes of data (such as satellite-based CDRs). They are often deployed on different spatial scales and consist of datasets dedicated for particular application. This makes them more accessible, easier to use and less costly for the users.
3.2. Data cubes to support wildfire risk analysis
To support the pilot activities, Ecere provided, as an in-kind contribution, a deployment of its GNOSIS Map Server implementing several OGC API standards enabling efficient access to data cubes. The API and backend functionality for these data cubes, improved throughout this pilot, are also supporting a Wildland Fire Fuel indicator workflow for the OGC Disaster Pilot taking place until the end of September 2023. As an end goal of that Disaster Pilot, the data cube API should support machine learning prediction for classifying wildland fire fuel vegetation type from Earth Observation imagery. A number of climate datasets and wildland fire danger indices were also made accessible through that same data cube API. Additional machine learning predictions experiments may be performed based on those datasets as well.
The API and datasets were provided in the hope that they would prove useful to other participants and could be part of Technology Integration Experiments (TIEs) for the pilot and other related OGC initiatives. Mainly due to the exploratory nature of this first phase of the pilot, no successful TIE with these resources with other participants were noted during its execution. However, these resources will remain operational, and successful TIEs are expected with them as part of the Disaster Pilot, the OGC Testbed 19 Geo Data Cube tasks as well as future phases of the climate resilience pilot.
3.2.1. Climate resilience data cubes
During the course of the pilot, the following datasets relevant to climate resilience were optimized and deployed at a data cube API demonstration end-point using the GNOSIS Map Server.
Table 2 — Datasets provided through GNOSIS Map Server data cube API
| Data collection | Fields | Temporal interval | Temporal resolution | Spatial extent | Spatial resolution | Additional dimension | Source |
|---|---|---|---|---|---|---|---|
| ESA sentinel-2 Level-2A | B01..B12, B8A, AOT, WVP, SCL | November 2016 to October 2022 | 5 days | Global (land only) | 10 meters | N/A | COGs and STAC catalogs on AWS |
| CMIP5 projections (wind speed) | Eastward and Northward wind velocity | 2016 to 2025 | daily | Global | 2.5° longitude x 2° latitude | 8 pressure levels | Copernicus Climate Data Store |
| CMIP5 projections (air temperature) | Air temperature | 2016 to 2025 | daily | Global | 2.5° longitude x 2° latitude | 8 pressure levels | Copernicus Climate Data Store |
| CMIP5 projections (geopotential height) | Geopotential height | 2016 to 2025 | daily | Global | 2.5° longitude x 2° latitude | 8 pressure levels | Copernicus Climate Data Store |
| CMIP5 projections on single level | Near-surface specific humidity, Precipitation, Snowfall flux, Sea level pressure, Surface downwelling shortwave radiation, Daily-mean near-surface wind speed, Average, Minimum and Maximum near-surface air temperature | 2016 to 2025 | daily | Global | 2.5° longitude x 2° latitude | N/A | Copernicus Climate Data Store |
| ERA5 reanalysis (relative humidity) | Relative humidity | April 1 to 6, 2023 | hourly | Global | 0.25° longitude x 0.25° latitude | 37 pressure levels | Copernicus Climate Data Store |
| ECMWF CEMS Fire Danger indices | Burning index, Build-up index, Danger risk, Drought code, Duff moisture code, Fire danger severity rating, Energy release component, Fire danger index, Fine fuel moisture code, Forest fire weather index, Ignition component, Initial spread index, Keetch-byram drought index, Spread component | January 2021 to July 2022 | daily | Global (except Antarctica) | 0.25° longitude x 0.25° latitude | N/A | Copernicus Climate Data Store |
| Fuel Vegetation Types for Continental United States | Fuel vegetation type | 2022 (no time axis) | N/A | Continental U.S. | ~20 meters | N/A | landfire.gov |
Figure 5 — ESA sentinel-2 Level-2A from COGs and STAC catalogs on AWS
Figure 6 — CMIP5 projections (air temperature) from Copernicus Climate Data Store
Figure 7 — ECMWF CEMS Fire Danger indices from Copernicus Climate Data Store
Figure 8 — Fuel Vegetation Types for Continental United States from landfire.gov
3.2.2. Overview of supported OGC API standards to access the data
The GNOSIS Map Server implements several published and candidate OGC API standards and is a certified implementation of OGC API — Features as well as OGC API — Processes. This section describes some of these supported standards and illustrates their use with requests for the climate data collections listed above.
3.2.2.1. OGC API — Common
The OGC API standards form a complementary set of functionality for efficiently accessing data and processing resources, combining together through the OGC API — Common framework. Whereas OGC API — Common — Part 1 standardizes how the API can present a landing page, describe itself and declare conformance to specific standards, Part 2 provides a consistent mechanism to list and describe collections of geospatial data. The following Common resources are available from the GNOSIS Map Server demonstration end-point:
| Resource | Common Part | URL |
|---|---|---|
| Landing page | Part 1 | https://maps.gnosis.earth/ogcapi |
| OpenAPI description | Part 1 | https://maps.gnosis.earth/ogcapi/api |
| Conformance declaration | Part 1 | https://maps.gnosis.earth/ogcapi/conformance |
| List of collections | Part 2 | https://maps.gnosis.earth/ogcapi/collections |
| Collection description | Part 2 | https://maps.gnosis.earth/ogcapi/collections/{collectionId} |
In addition to the common resources standardized by Part 1 and Part 2, several API building blocks are consistently re-used across the different OGC API standards. The following table summarizes common query parameters supported by several of the data access APIs:
| Query parameter | Description | APIs |
|---|---|---|
| subset | For subsetting (trimming or slicing) on an arbitrary dimension | Coverages, Maps, Tiles (except for spatial dimensions), DGGS (zone query; for data retrieval: except for DGGS dimensions) |
| bbox | For subsetting on spatial dimensions (Features: spatial intersection) | Coverages, Maps, DGGS (zone query), Features |
| datetime | For subsetting on temporal dimension (Features: temporal intersection) | Coverages, Maps, Tiles, DGGS (data retrieval: except for temporal DGGS), Features |
| properties | For selecting specific properties to return (range subsetting); deriving new fields (properties) using CQL2 expression | Coverages, Tiles, DGGS, Features |
| filter | For filtering using a CQL2 expression | Coverages, Maps, Tiles, DGGS, Features |
| crs | For selecting an output coordinate reference system | Coverages, Maps, Features |
| bbox-crs | For specifiying the coordinate reference system of the bbox parameter | Coverages, Maps, Features, DGGS |
| subset-crs | For specifiying the coordinate reference system of the subset parameter | Coverages, Maps, DGGS |
| width | For specifying the width of the output (resampling) | Coverages, Maps |
| height | For specifying the height of the output (resampling) | Coverages, Maps |
With Coverages and Maps, a spatial area of interest can be specified using either e.g., bbox=10,20,30,40 or subset=Lat(20:40),Lon(10:30).
For temporal datasets, a specific time can be requested using e.g., datetime=2022-03-01 or subset=time("2022-03-01").
For the data cubes with multiple pressure levels, the pressure dimension is defined and can be used with the subset query parameter with all of the data access OGC API standards (Coverages, Tiles, DGGS and Maps) e.g., subset=pressure(500).
3.2.2.2. OGC API — Coverages
The OGC API — Coverages candidate Standard is a simple API defining fundamental functionality to retrieve access data for arbitrary fields, area, time and resolution of interest from a data cube.
The main resource to retrieve data using the Coverages API is located at /collections/{collectionId}/coverage for each data collection. This resource supports a number of query parameters defined by optional requirements classes and extensions supported by the GNOSIS Map Server:
| Query parameter | Description | Requirements class |
|---|---|---|
| subset | For subsetting (trimming or slicing) on an arbitrary dimension | Subsetting |
| bbox | For subsetting on spatial dimensions | Subsetting |
| datetime | For subsetting on temporal dimension | Subsetting |
| scale-factor | For resampling using the same factor for all dimensions (1: no resampling, 2: 2x downsampling) | Scaling (resampling) |
| scale-axes | For resampling using a specific factor for individual dimensions | Scaling (resampling) |
| scale-size | For resampling by specifying the expected number of cells for each dimension | Scaling (resampling) |
| width | For specifying the width of the output (resampling) | Scaling (resampling) |
| height | For specifying the height of the output (resampling) | Scaling (resampling) |
| properties | For selecting specific properties to return (range subsetting); deriving new fields using CQL2 expression | Range subsetting; Derived fields extension |
| filter | For filtering using a CQL2 expression | Range filtering extension |
| crs | For selecting an output coordinate reference system | CRS extension |
| bbox-crs | For specifiying the coordinate reference system of the bbox parameter | CRS extension |
| subset-crs | For specifiying the coordinate reference system of the subset parameter | CRS extension |
The Coverages draft currently also specifies a DomainSet JSON object which is linked using the [ogc-rel:coverage-domainset] link relation from the collection description, which may be included either within the collection description itself, or at a dedicated resource (/collections/{collectionId}/coverage/domainset). The schema for this DomainSet object describes the domain of the coverage (the extent and resolution of its dimensions / axes) and follows the Coverages Implementation Schema (CIS) 1.1.1. An example of such a domain set resource can be found at https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:windSpeed/coverage/domainset?f=json . At the time of writing this report, discussions are underway to potentially simplify the API by fully describing the domain directly within the collection description resource, using uniform additional dimensions, as well as the grid property, inside the extent property, which can describe both regular as well as irregular grids, removing the need for this extra resource. For example, see the collection description for the CMIP5 single pressure level data and its corresponding CIS domain set resource.
The Coverages draft currently also specifies a RangeType JSON object which is linked using the [ogc-rel:coverage-rangetype] link relation from the collection description, which may be included either within the collection description itself, or at a dedicated resource (/collections/{collectionId}/coverage/domainset). The schema for this RangeType object describes the range type of the coverage (the extent and resolution of its dimensions / axes) and follows the Coverages Implementation Schema (CIS) 1.1.1. An example of such a range type resource can be found at https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:windSpeed/coverage/rangetype?f=json . It might be possible to also describe the range type in a common way across the different OGC APIs using a JSON schema with semantic annotations, as per the work undertaken for OGC API — Features — Part 5: Schemas.
A Coverage Tiles requirements class is defined in OGC API — Coverages, leveraging the OGC API — Tiles standard while clarifying requirements for coverage tile responses. Example of coverage tiles requests are described below in the OGC API — Tiles section.
At the moment, the GNOSIS Map Server implementation of Coverages is limited to the following 2D (spatial dimensions) output formats:
GeoTIFF (multiple fields, two-dimensional),
PNG (single field, 16-bit output, currently using fixed scale (2.98) and offset (16384) modifiers).
There is a plan to add support for n-dimensional output formats, including netCDF, CIS JSON and eventually CoverageJSON as well. For coverages with more than two dimensions, a specific time and/or pressure slice must therefore be selected, currently requiring separate API requests to retrieve a range of time or pressure levels.
Some example of coverage requests:
https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage?f=geotiff=tas,tasmax,tasmin,pr,psl=Lat(-90:90),Lon(0:180)=400=2020-05-20 (GeoTIFF coverage with 5 bands for each field)
https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/coverage?f=geotiff&subset=pressure(750) (GeoTIFF Coverage)
Figure 9 — Coverage request for CMIP5 maximum daily temperature
3.2.2.3. OGC API — Maps
The OGC API — Maps candidate Standard defines the ability to retrieve a visual representation of geospatial data. The main resource to retrieve data using the Maps API is located at /collections/{collectionId}/map for each data collection. This resource supports a number of query parameters defined by optional requirements classes and extensions supported by the GNOSIS Map Server:
| Query parameter | Description | Requirements class |
|---|---|---|
| bbox | For subsetting on spatial dimensions | Spatial Subsetting |
| bbox-crs | For specifiying the coordinate reference system of the bbox parameter | Spatial Subsetting |
| subset | For subsetting (trimming or slicing) on an arbitrary dimension | Spatial/Temporal/General Subsetting |
| subset-crs | For specifiying the coordinate reference system of the subset parameter | Spatial/Temporal/General Subsetting |
| datetime | For subsetting on temporal dimension | Temporal Subsetting |
| width | For specifying the width of the output (resampling) | Scaling (resampling) |
| height | For specifying the height of the output (resampling) | Scaling (resampling) |
| crs | For selecting an output coordinate reference system | CRS |
| bgcolor | For specifiying the color of the background | Background |
| transparent | For specifiying whether the background should be transparent | Background |
| filter | For filtering using a CQL2 expression | Filtering extension |
Some example map requests:
NOTE: Proper symbolization for this wind velocity map (above request) would require support for wind barbs. In the meantime, the Eastward and Northward velocity are assigned to the green and blue color channels.
Figure 10 — Sentinel-2 map (natural color)
Some example map requests for a specific style, in conjunction with OGC API — Styles:
Figure 11 — Sentinel-2 map for NDVI style
Figure 12 — Sentinel-2 map for Scene Classification Map style
A Map Tilesets requirements class is defined in OGC API — Maps, leveraging the OGC API — Tiles stand while clarifying requirements for map tile responses. Example of map tiles requests are described below in the OGC API — Tiles section.
3.2.2.4. OGC API — Tiles
The OGC API — Tiles Standard defines the ability to retrieve geospatial data as tiles based on the OGC 2D Tile Matrix Set and Tileset Metadata Standard, originally defined as part of the Web Map Tile Service (WMTS) Standard. Unlike WMTS which focused strictly on pre-rendered or server-side rendered Map tiles, the Tiles API was designed to also enable the use of data tiles such as Coverages Tiles and Vector Tiles which can be styled, rendered and used for data analytics performed on the client side. Using pre-determined partitioning schemes facilitates caching for both servers and clients, resulting in more responsive dynamic maps.
The following Tiles API resources are defined:
| Resource | Requirements Class | Description |
|---|---|---|
| …/tiles | Tilesets list | List of available tilesets |
| …/tiles/{tileMatrixSetId} | Tileset | Description of tileset and link to 2D Tile Matrix Set definition |
| …/tiles/{tileMatrixSetId}/{tileMatrix}/{tileRow}/{tileCol} | Core | Tiles for a given Tile 2D Matrix Set, tile matrix/row/column |
The GNOSIS Map Server supports a number of 2D Tile Matrix Sets for all of the collections it hosts, including:
the GNOSIS Global Grid (EPSG:4326),
WorldCRS84Quad (EPSG:CRS84 / EPSG:4326),
WebMercatorQuad (EPSG:3857),
WorldMercatorWGS84Quad (EPSG:3395),
3.2.2.4.1. Coverage Tiles
The GNOSIS Map Server currently supports the following coverage tile formats:
GNOSIS Map Tiles (multiple fields, n-dimensional)
GeoTIFF (multiple fields, two-dimensional)
PNG (single field, 16-bit value using fixed scale (2.98) and offset (16384) modifiers)
Support is planned for netCDF, CIS JSON, and eventually CoverageJSON as well as additional formats.
Example coverage tile queries:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/GNOSISGlobalGrid/3/4/17
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/ISEA9Diamonds/4/373/288
To request a different sentinel-2 band than the default RGB (B04, B03, B02) bands:
Figure 13 — Sentinel-2 PNG coverage tile for band 08 (near infra-red)
https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage/tiles/WebMercatorQuad/1/1/0?f=geotiff=2022-09-04 (GeoTIFF coverage tile)
https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/coverage/tiles/WorldCRS84Quad/0/0/0?f=geotiff=pressure(750) (GeoTIFF coverage tile)
3.2.2.4.2. Map Tiles
The GNOSIS Map Server currently supports the following map tile formats:
PNG (RGBA)
JPEG
GeoTIFF
Some example of map tiles queries:
Figure 14 — Sentinel-2 Level-2A map tile for GNOSISGlobalGrid level 3, row 4, column 17
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/GNOSISGlobalGrid/3/4/17
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/ISEA9Diamonds/4/373/288
To get map tiles from a predefined style, in conjunction with OGC API — Styles:
Figure 15 — Sentinel-2 Level-2A map tile using Scene Classification Map style for GNOSISGlobalGrid level 3, row 4, column 17
Figure 16 — Sentinel-2 Level-2A map tile using NDVI style for GNOSISGlobalGrid level 3, row 4, column 17
3.2.2.5. OGC Common Query Language (CQL2)
The OGC Common Query Language, abbreviated CQL2, allows to define query expressions. Although introduced as a language to specify a boolean predicate for OGC API — Features — Part 3: Filtering, the language is easily extended for additional use cases such as filtering the range set of a coverage request, or to deriving new fields using expressions (that can return non-boolean values) including performing coverage band arithmetics, such as calculating vegetation indices.
Support for CQL2 in the filter parameter is implemented in the GNOSIS Map Server for Coverages, Features, Maps, Tiles as well as DGGS. For example, to request all data from the CMIP5 single pressure level collection where the maximum daily temperature is greater than 300 Kelvins, filter=tasmax>300 (unmatched cells will be replaced by NODATA values).
Support for CQL2 in the properties parameter is currently implemented for Coverages, Tiles and DGGS. For example, the pr precipitation property can be multiplied by a factor of one thousand using properties=pr*1000.
Using a CQL2 expression to filter out the clouds in a map tile:
Figure 17 — Sentinel-2 map tile filtered by Scene Classification Layer to remove clouds (a longer time interval with fewer clouds would be necessary to complete the mosaic)
Using a CQL2 expression in coverage tile requests to perform band arithmetic computing NDVI:
Figure 18 — Coverage tile request from sentinel-2 computing NDVI
Using a CQL2 expression in a coverage request to multiply the relative humidity and filter resulting values below a threshold (20):
Figure 19 — Coverage request from relative humidity coverage multiplying r by 200 and returning only values where r > 20
3.2.2.6. OGC API — Discrete Global Grid Systems
The OGC API — DGGS candidate Standard allows to retrieve data and perform spatial queries based on hierarchical multi-resolution discrete grids covering the entirety of the Earth. There are three main requirements classes for this standard:
Core (DGGS definition and zone information resource),
Zone Data Retrieval (What is here?),
Zones Query (Where is it?)
The following DGGS API resources are defined:
| Resource | Requirements Class | Description |
|---|---|---|
| …/dggs | Core | List of available DGGSs |
| …/dggs/{dggsId} | Core | Description and link to definition of a specific DGGS |
| …/dggs/{dggsId}/zones | Zone Query | For retrieving the list of zones matching a collection and/or query |
| …/dggs/{dggsId}/zones/{zoneId} | Core | For retrieving information about a specific zone |
| …/dggs/{dggsId}/zones/{zoneId}/data | Data Retrieval | For retrieving data for a specific zone |
DGGS API requests imply the use a particular grid understood by both the client and the server, associated with the {dggsId} of the resource on which the request is performed. Several different discrete global grids have been defined. The GNOSIS Map Server currently supports two discrete global grids:
the GNOSIS Global Grid, based on the 2D Tile Matrix Set of the same name defined in the EPSG:4326 geographic CRS, axis-aligned with latitude and longitude, and using variable width tile matrices to approach equal area (maximum variation is ~48% up to a very detailed zoom level),
the ISEA9R (Icosahedral Snyder Equal Area aperture 9 Rhombus) grid, a dual DGGS of ISEA3H (aperture 3 hexagonal) for its even levels, using rhombuses/diamonds which, compared to hexagons, are much simpler to index and for which it is much easier to encode data in a rectilinear formats such as GeoTIFF. The area values of ISEA3H hexagons can be transported as points on the rhombus vertices for those ISEA3H even levels. The ISEA9R grid is also axis-aligned to a CRS defined by rotating and skewing the ISEA projection, also allowing to define a 2D Tile Matrix Set for it.
A client will normally opt to use OGC API — DGGS if it shares an understanding and internal use of the same grid with the server. Although for axis-aligned DGGS that can be represented as a 2D Tile Matrix Set OGC API — Tiles can be used to retrieve data for specific zones, the DGGS API enables zone data retrieval for other DGGS which are not axis-aligned or whose geometry makes that impossible (e.g., hexagons). Another important use of the DGGS API is the ability to efficiently retrieve the results of a spatial query (e.g., using CQL2) in the form of a compacted list of zone IDs.
3.2.2.6.1. Core
The core requirements class defines requirements to list available DGGS, describe each of them and provide information for individual zones.
In the GNOSIS Map Server implementation of the zone information resource, since both supported DGGS also correspond to a 2D Tile Matrix Set, the Level, Row and Column for the equivalent OGC API — Tiles request is displayed on the information page, as can be seen below. For the DGGS {zoneId}, the level, row and column is encoded differently in a compact hexadecimal identifier. Some example zone information requests:
Figure 20 — GNOSIS Map Server information resource for GNOSIS Global Grid zone 5-24-6E
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/GNOSISGlobalGrid/zones/5-24-6E
Figure 21 — GNOSIS Map Server information resource for ISEA9Diamonds zone A7-0
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/ISEA9Diamonds/zones/A7-0
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/ISEA9Diamonds/zones/E7-FAE
3.2.2.6.2. Zone Data Retrieval: What is here?
The Zone Data Retrieval requirements class allows to retrieve data for a specific DGGS zone. For axis-aligned DGGSs whose zone geometry can be described by a 2D Tile Matrix Set such as the GNOSISGlobalGrid, ISEA9R or rHealPix, this capability is equivalent to Coverage Tiles requests for the corresponding TileMatrixSets. This requirements class supports returning data for zones whose geometry is of an arbitrary shape e.g., hexagonal or triangular. The zone data retrieval resource is …/dggs/{dggsId}/zones/{zoneId}/data, for which the GNOSIS Map Server supports a number of query parameters:
| Query parameter | Description |
|---|---|
| filter | For filtering data within the response using a CQL2 expression |
| properties | For selecting specific properties to return (range subsetting); deriving new fields using CQL2 expression |
| datetime | For subsetting on temporal dimension |
| subset | For subsetting (trimming or slicing) on an arbitrary dimension (besides the DGGS dimensions) |
| subset-crs | For specifiying the coordinate reference system of the subset parameter |
| zone-depth | For specifying zone depths to return relative to the requested zone (0 corresponding to a single set of values for the zone itself) |
Some example of data retrieval queries:
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/GNOSISGlobalGrid/zones/3-4-11/data
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/ISEA9Diamonds/zones/E7-FAE/data
3.2.2.6.3. Zone Queries: Where is it?
The Zone Query requirements class allows to efficiently retrieve the results of a spatial query in the form of compact list of zone IDs. The list can be compacted (the default) by replacing children zones by their parents when all children of that parent are part of the result set. The zone query resource is …/dggs/{dggsId}/zones, for which the GNOSIS Map Server supports a number of query parameters:
| Query parameter | Description |
|---|---|
| zone-level | For specifying the desired zone hierarchy level for the resulting list of zone IDs |
| compact-zones | For specifying whether to return a compact list of zones (defaults to true) |
| filter | For filtering using a CQL2 expression |
| datetime | For subsetting on temporal dimension |
| bbox | For subsetting on spatial dimensions |
| bbox-crs | For specifiying the coordinate reference system of the bbox parameter |
| subset | For subsetting (trimming or slicing) on an arbitrary dimension |
| subset-crs | For specifiying the coordinate reference system of the subset parameter |
By creating a kind of mask at a specifically requested resolution level, DGGS Zones Query can potentially greatly help parallelization and orchestration of spatial queries combining multiple datasets across multiple services, allowing to perform early optimizations with lazy evaluation.
NOTE: There are currently some limitations to the GNOSIS Map Server implementation of the Zones Query requirements class.
Examples of zone queries:
Where is relative humidity at 850 hPa greater than 80% on April 3rd, 2023? (at precision level of GNOSIS Global Grid level 6)
(using the default compact-zones=true where children zones are replaced by parent zone if all children zones are included)
https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones?subset=pressure(850)=2023-04-03=r%3E80=6=json (Plain Zone ID list output)
https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones?subset=pressure(850)=2023-04-03=r%3E80=6=uint64 (Binary 64-bit integer Zone IDs)
https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones?subset=pressure(850)=2023-04-03=r%3E80=6=geotiff (GeoTIFF output)
Figure 22 — GeoJSON output of a GNOSIS Global Grid DGGS Zone Query for relative humidity at 850 hPa greater than 80% on April 3rd, 2023
Where is maximum daily temperature greater than 300 degrees Kelvins on September 4, 2022? (at precision level of GNOSIS Global Grid level 6)
(using the default compact-zones=true where children zones are replaced by parent zone if all children zones are included)
https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/dggs/GNOSISGlobalGrid/zones?filter=tasmax%3E300=2022-09-04=6=json (Plain JSON Zone ID list output)
https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/dggs/GNOSISGlobalGrid/zones?filter=tasmax%3E300=2022-09-04=6=uint64 (Binary 64-bit integer Zone IDs)
https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/dggs/GNOSISGlobalGrid/zones?filter=tasmax%3E300=2022-09-04=6=geotiff (GeoTIFF output)
Figure 23 — GeoJSON output of a GNOSIS Global Grid DGGS Zone Query for maximum daily temperature greater than 300 degrees Kelvins on September 4, 2022
Additional examples of zone queries for a Digital Elevation Model (returning regions where elevation data is available):
https://maps.gnosis.earth/ogcapi/collections/SRTM_ViewFinderPanorama/dggs/ISEA9Diamonds/zones
https://maps.gnosis.earth/ogcapi/collections/SRTM_ViewFinderPanorama/dggs/ISEA9Diamonds/zones?f=json (as a list of compact JSON IDs)
3.2.2.7. OGC API — Processes — Part 1: Core
The OGC API — Processes standard defines the capability to execute remote processes accepting inputs and returning outputs.
A list of processes is available from the GNOSIS Map Server demonstration end-point at https://maps.gnosis.earth/ogcapi/processes . The following table summarizes the available processes and their current functionality status.
| Process | Status | Description |
|---|---|---|
| Features Attributes Combiner | Working | This process augments existing vector features with attributes available from a separate feature collection based on an attribute key. |
| Elevation contours tracer | Working | This process computes contours over an elevation coverage, uniformly spaced by a given vertical distance. |
| Processes — Core / Modular OGC API Workflows adapter | Working | This process enables the integration of servers supporting OGC API — Processes — Part 1: Core within a modular workflow. |
| OSM Ecere Routing Engine (OSMERE) | Working | This process computes a route from waypoints based on an OSM roads network. |
| Maps rendering process | Working | This process renders a map from input data layers. |
| Passthrough process | Working for features (coverage support to implement) | This process integrates inputs passing them through as an output, providing an opportunity to apply field modifiers. |
| Echo Process | Working (passing TeamEngine CITE test) | This process accepts any number of input and simply echoes each input as an output. |
| Point Cloud Gridifier | (Currently requires a local Point Cloud collection, and none is loaded) | Generate a Digital Elevation Model or orthorectified imagery from a point cloud |
| Point Cloud Elevation | (Currently requires a local Point Cloud collection, and none is loaded) | This process extracts elevation values from a point cloud and applies them as attributes to vector features. |
| Random Forest Classification | (To be tested again with local sentinel2-l2a collection) | Output random-forest classified image using imagery and training feature dataset |
| MOAW-WCPS adapter | (To be tested again with WCPS implementation) | This process integrates a WCPS service as part of a Modular OGC API Workflow. |
The description of each individual process is available at /processes/{processId}, listing available inputs and outputs, whereas the execution end-point for each process is at /processes/{processId}/execution, supporting a POST operation in which the client includes an execution request as a payload. At this time, only synchronous execution and (Part 3) collection output deferred execution is supported.
A new process is being developed to classify fuel vegetation types using machine learning prediction in the context of the OGC Disaster Pilot 2023. This process will accept as input data from the sentinel-2 Level-2A collection and return fuel vegetation types. The fuel vegetation type coverage for continental United States from landfire.gov will be used as initial training data. This process was not yet operational at the time of writing this report.
3.2.2.8. OGC API — Processes — Part 3: Workflows and Chaining
The Part 3: Workflows and Chaining candidate Standard extends OGC API — Processes enabling the chaining of nested local and remote processing capabilities, and their integration with local and remote OGC API data collections.
The GNOSIS Map Server currently supports the following extensions defined by Part 3: Workflows and Chaining to the process execution capabilities of Part 1:
extending execution requests submitted to /processes/{processId}/execution by:
referencing local and remote nested processes as inputs ("process"),
referencing local and remote OGC API collections as inputs ("collection"),
modifying data accessed as inputs and returned as outputs (currently only for the PassThrough process) by filtering with "filter", as well as selecting and deriving fields with "properties",
requesting output data from virtual OGC API data collections to trigger processing execution (collection output), using response=collection query parameter and value.
Work is ongoing to enhance the data integration capabilities and cross-collection queries to achieve the full potential of Part 3 bringing together local and remote OGC API data and processing capabilities.
4. Raw data to Analysis Ready Data (ARD)
CEOS defines Analysis Ready Data as satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets. See https://ceos.org/ard/, and especially the information for data producers: https://ceos.org/ard/files/CARD4L_Info_Note_Producers_v1.0.pdf.
4.1. Transforming climate relevant raw data to ARD
Several past successful OGC testbeds, including the DP 21 to which this pilot is linked, have looked at ARD and IRD but also in terms of use cases. In this pilot, some main technical contributions have been creating digestible OGC data types and formats for specific partner use cases, so producing ARD from publically available EO and model data, including hydrological and other type of model output as well as climate projections.
These ARD will feed into all use cases for all participants, with a particular focus toward the use cases proposed for Heat, Drought and Health Impacts by participants in the pilot.
Specifically, participants provide access to the following satellite and climate projection data:
Wildfire: Fire Radiant Power (FRP) product from Sentinel 3 (NetCDF), 5p, MODIS products (fire detection), VIIRS (NOAA); possibly biomass availability (fire fuel).
Land Surface Temp — Sentinel 3
Pollution — Sentinel 5p
Climate Projection data (NetCDF, etc., daily downscaled possible): air temp (10 m above ground). Rainfall and possibly wind direction as well
Satellite-derived Discharge Data to look at Droughts/Floods etc. by basin or other scale
Hydrological model simulation outputs at (sub)basin scale (within reason)
The created ARD in various OGC interoperable formats created digestible dataflows for the proposed OGC Use Cases. This proposed data chain by several participants is similar to DP21, in which contributors, like RSS-Hydro, SafeSoftware, and others also participated. A generated climate indicator or EO remotely sensed data (NASA, NOAA, ESA, etc.) from various sources are “simplified”to GeoTIFF and / or vectorized geopackage per time step by other participants’ tools, such as the FME software (by SafeSoftware). Another option as an intermediate data type (IRD) can be COG — cloud optimized geotiff which would make access more efficient. The COG GeoTIFFs are optimized for cloud so data sharing can be made more efficient. ARD and IRD should become more service / cloud based wherever possible.
Besides the data format, data structures and semantics required to support the desired DRI’s are important. The time series / raster, and classification to vector contour transform is an approach that worked well in DP21 and has been a good starting point also in this pilot. For example, together in the FME processing engine, time series grids can be aggregated across timesteps to mean or max values, then classify them into ranges suitable for decision making, and then write them out and expose them as time tagged vector contour tables.
In summary, the different ARD and IRD data can be created from the following data sources:
Inputs: EO (US sources fire related: MODIS, VIIRS); Climate projections, sub catchment polygons, ESA sources; Sentinel-3, Sentinel 5-P.
Outputs forma & instances: WCS, GeoTIFF spatial / temporal subset, Shape; NetCDF.
Output parameters: e.g. hydrological condition of a basin (historically/current). So drought / flood etc.
Output themes: downscaled / subset outputs, hydrologic scenarios.
Another highly relevant input are the Essential Climate Variables (ECV) Inventory (https://climatemonitoring.info/ecvinventory/) houses information on Climate Data Records (CDR) provided mostly by CEOS and CGMS member agencies. The inventory is a structured repository for the characteristics of two types of GCOS ECV CDRs: * Climate data records that exist and are accessible, including frequently updated interim CDRs; * Climate data records that are planned to be delivered.
The ECV Inventory is an open resource to explore existing and planned data records from space agency sponsored activities and provides a unique source of information on CDRs available internationally. Access links to the data are provided within the inventory, alongside details of the data’s provenance, integrity and application to climate monitoring.
Participants, particularly GMU CSISS have demonstrated the use of ECV record information as input with OpenSearch service endpoint (currently CMR(CWIC) and FedEO), and downloading URLs for accessing NetCDF or HDF files.
Outputs in this case include WCS service endpoint for accessing selected granule level product images (GeoTIFF, PNG, JPEG, etc.), focusing on the WCS for downloading images and WMS for showing layers on a basemap.
4.3. From Raw Data and Data Cubes to ARD with the FME Platform
4.3.1. Component Descriptions
D100 — Client instance: Analysis Ready Data Component
Our Analysis Ready Data component (ARD) uses the FME platform to consume regional climate model and EO data and generate FAIR datasets for downstream analysis and decision support.
The challenge to manage and mitigate the effects of climate change poses difficulties for spatial and temporal data integration. One of the biggest gaps to date has been the challenge of translating the outputs of global climate models into specific impacts at the local level. FME is ideally suited to help explore options for bridging this gap given its ability to read datasets produced by climate models such as NetCDF or OGC WCS and then filter, aggregate, interpolate and restructure it as needed. FME can inter-relate it with higher resolution local data, and then output it to whatever format or service is most appropriate for a given application domain or user community.
Our ARD component supports the consumption of climate model outputs such as NetCDF. It also has the capacity to consume earth observation (EO) data, and the base map datasets necessary for downstream workflows, though given time and resource constraints during this phase we did not pursue consumption of other data types besides climate data.
4.3.1.1. ARD Workflow
The basic workflow for generating output from the FME ARD component is as follows. The component extracts, filters, interrelates and refines these datasets according to indicator requirements. After extraction, datasets are filtered by location and transformed to an appropriate resolution and CRS. Then the workflow resamples, simplifies and reprojects the data, and then defines record level feature identifiers, ECV values, metadata and other properties to satisfy the target ARD requirements. This workflow is somewhat similar to what was needed to evaluate disaster impacts in DP21. Time ranges for climate scenarios are significantly longer — years rather than weeks for floods.
Once the climate model, and other supporting datasets have been adequately extracted, prepared and integrated, the final step is to generate the data streams and datasets required by downstream components and clients. The FME platform is well suited to deliver data in formats as needed. This includes Geopackage format for offline use. For online access, other open standards data streams are available, such as GeoJSON, KML or GML, via WFS and OGC Features APIs and other open APIs. For this pilot we generated OGC Geopackage, GeoJSON, CSV and OGC Features API services.
Figure 29 — High level FME ARD workflow showing generation of climate scenario ARD and impacts from climate model, EO, IoT, infrastructure and base map inputs
As our understanding of end user requirements continues to evolve, this will necessitate changes in which data sources are selected and how they are refined, using a model based rapid prototyping approach. We anticipate that any operational system will need to support a growing range of climate change impacts and related domains. Tools and processes must be able to absorb and integrate new datasets into existing workflows with relative ease. As the pilot develops, data volumes increase, requiring scalability methods to maintain performance and avoid overloading downstream components. Cloud based processing near cloud data sources using OGC API web services supports data scaling. Regarding the FME platform, this involves deployment of FME workflows to FME Cloud. Note that in future phases, we are likely to test how cloud native datasets (COG, STAC, ZARR) and caching can be used to scale performance as data transactions and volume requirements increase.
It is worth underlining that our ARD component depends on the appropriate data sources in order to produce the appropriate decision ready data (DRI) for downstream components. Risk factors include being able to locate and access suitable climate models of sufficient quality, resolution and timeliness to support indicators as the requirements and business rules associated with them evolve. Any data gaps encountered are documented under this section under Challenges and Opportunities and in the common Lessons Learned chapter and the end of the ER.
Figure 30 — Environment Canada NetCDF GCM time series downscaled to Vancouver area. From: https://climate-change.canada.ca/climate-data/#/downscaled-data
Figure 31 — Data Cube to ARD: NetCDF to KML, Geopackage, GeoTIFF
Original Data workflow: - Split data cube - Set timestep parameters - Compute timestep stats by band - Compute time range stats by cell - Classify by cell value range - Convert grids to vector contours
Figure 32 — Extracted timestep grids: Monthly timesteps, period mean T, period max T
Figure 33 — Convert raster temperature grids into temperature contour areas by class
Figure 34 — Geopackage Vector Area Time Series: Max Yearly Temp
4.3.1.2. ARD Development Observations
Figure 35 — FME Data Inspector: RCM NetCDF data cube for Manitoba temperature 2020-2099
Disaster Pilot 2021 laid a good foundation for exploring data cube extraction and conversion to ARD with using the FME data integration platform. A variety of approaches were explored for extraction, simplification and transformation including approaches to select, split, aggregate, and summarize time series. However, more experimentation was needed to generate ARD that can be queried to answer questions about climate trends. This evolution of ARD was one of the goals for this CRP. This goal includes better support for both basic queries, and analytics, statistical methods, simplification & publication methods, including cloud native — NetCDF to Geopackage, GeoJSON and OGC, APIs.
In consultation with other participants, we learned fairly early on in the pilot that our approach to temperature and precipitation contours or polygons inherited from our work in DP21 on flood contours involved too much data simplification to be useful. For example, contouring required grid classification into segments, such as 5 degree C or 10mm of precipitation etc. However, this effective loss of detail oversimplified the data to the point where it no longer held enough variation over local areas to be useful. In discussion with other participants, it was determined that simply converting multidimensional data cubes to vector time series point data served the purpose of simplifying the data structure for ease of access, but retained the ECV precision needed to support a wider range of data interpretations for indicator derivation. It also meant that as a data provider we did not need to anticipate or encode interpretation of indicator business rules into our data simplification process. By simply providing ECV data points, the end user was free to run queries to find locations and time steps where temp > or precipitation < some threshold of interest.
Initially it was thought that classification rules need to more closely model impacts of interest. For example, the business rules for a heat wave might use a temperature range and stat type as part of the classification process before conversion to vector. However, this imposes the burden of domain knowledge on the data provider rather than on the climate service end user who is much more likely to understand the domain they wish to apply the data to and how best to interpret it.
Modified ARD Data workflow:
Split data cube
Set timestep parameters
Compute timestep stats by band
Compute time range stats by cell
Convert grids to vector points”
Further scenario tests were explored, including comparison with historical norms. Calculations were made using the difference between projected climate variables and historical climate variables. These climate variable deltas may well serve as a useful starting point for climate change risk indicator development. They also serve as an approach for normalizing climate impacts when the absolute units are not the main focus. Interesting patterns emerged for the LA area that we ran this process on deltas between projected and historical precipitation. While summers are typically dry and winters are wet and prone to flash floods. Initial data exploration seemed to show an increase in drought patterns in the spring and fall. More analysis needs to be done to see if this is a general pattern or simply one that emerged from the climate scenario we ran. However, this is the type of trend that local planners and managers may benefit from having the ability to explore once they have better access to climate model scenario outputs along with the ability to query and analyze them.
Figure 36 — Modified ARD Workflow: NetCDF data cube to precipitation delta grids (future - historical) in Geopackage for LA
ARD Climate Variable Delta Data workflow:
Split data cubes from historic and future netcdf inputs
Set timestep parameters
Compute historic mean for 1950 — 1980 per month based on historic time series input
Multiply historic mean by -1
Use RasterMosaiker to sum all future grids with -1 * historic mean grid for that month
Normalize environmental variable difference by dividing by historic range for that month delta / (max — min)
Convert grids to vector contours
Define monthly environment variables from band and range values
More analysis needs to be done with higher resolution time steps — weekly and daily. At the outset monthly time steps were used to make it easier to prototype workflows. Daily time step computations will take significantly more processing time. Future pilots should explore ways of better supporting scalability of processing through automation and cloud computing approaches such as the use of cloud native formats (STAC, COG, ZARR etc).
4.3.1.3. OGC API Features Service
Compared to OGC WFS2, OGC APIs are a simpler and more modern standard based on a REST and JSON / openAPI approach. However we found implementation of OGC API services somewhat challenging. There seems to be more complexity in terms of number of ways for requesting features, and too many options for representing service descriptions. As every client tends to interpret and use the standard a bit differently — it becomes a challenge to derive how to configure service for a wide range of clients. In particular, QGIS / ArcPro were a challenge to debug given limited logging. For QGIS, we had to examine cache files in the operating system temp directories to look for and resolve errors.
Once correctly configured, OGC API feature services seemed to perform well and likely are more efficient than the equivalent WFS2 / GML feature services. A key aspect of performance improvement was achieving query parameter continuity by passing query settings from the client all the way to the database reader configuration. For example, it was important to make sure the spatial extent and feature limits from the end user client were implemented in the database SQL extraction query and not just at an intermediate stage. We will need to explore better use of caching to further optimize performance. There may also be opportunities for pyramiding time series vector data at a lower resolution for wide area requests. This may better serve those interested in observing large area patterns who don’t necessarily need full resolution at the local level.
It should also be noted that while OGC API services should be a priority for standards support, for a climate and disaster management context, given the relative recent nature of these standards many users may be less than familiar with or prepared to use these standards. As such, there should also be provision to access data directly in well accepted open standards such as GeoJSON, CSV, GeoTIFF, Geopackage or Shape. In this project, some users preferred direct access to GeoJSON or CSV over OGC API access.
4.4. A framework example for climate ARD generation
4.4.1. Component: Surface Reflectance ARD
Inputs: Gaofen L1A data and Sentinel-2 L1C data
Outputs: Surface Reflectance ARD
What other component(s) can interact with the component: Any components requiring access to surface reflectance data
Surface Reflectance (SR) is the fraction of incoming solar radiation reflected from the Earth’s surface for specific incidents or viewing cases. It can be used to detect the distribution and change of ground objects by leveraging the derived spectral, geometric, and textural features. Since a large amount of optical EO data has been released to the public, ARD can facilitate interoperability through time and multi-source datasets. As the probably most widely applied ARD product type, the SR ARD can contribute to climate resilience research. For example, the SR-derived NDVI series can be applied to monitor wildfire recovery by analyzing vegetation index increases. Several SR datasets have been assessed as ARD by CEOS, like the prestigious Landsat Collection 2 Level 2, and Sentinel-2 L2A, while many other datasets are still provided at a low processing level.
WHU is developing a pre-processing framework for SR ARD generation. The framework supports radiometric calibration, geometric ratification, atmospheric correction, and cloud mask. To address the inconsistencies in observations from different platforms, including variations in band settings and viewing angles, we proposed a processing chain to produce harmonized ARD. This will enable us to generate SR ARD with consistent radiometric and geometric characteristics from multi-sensor data, resulting in improved temporal coverage. In the first stage of our mission, we are focusing on the harmonization of Chinese Gaofen data and Sentinel-2 data, as shown in Figure 37, the harmonization involves spatial co-registration, band conversion, and bidirectional reflectance distribution function (BRDF) correction. Figure 38 shows the Sentinel-2 data before and after pre-processing. Furthermore, we wish to seek the assessment of CEOS-ARD in our long-term plan.
Figure 37 — The processing chain to produce harmonized ARD.
Figure 38 — Sentinel-2 RBG composite (red Band4, green Band3, blue Band2), over Hubei, acquired on October 22, 2020. (a) corresponds to the reflectance at the top of the atmosphere (L1C product); (b) corresponds to the surface reflectance after pre-processing.
4.4.2. Component: Drought Indicator
Inputs: Climate data, including precipitation and temperature
Outputs: Drought risk map derived from drought indicator
What other component(s) can interact with the component: Any components requiring access to drought risk map through OGC API
What OGC standards or formats does the component use and produce: OGC API — Processes
Drought is a disaster whose onset, end, and extent are difficult to detect. Original meteorological data, such as precipitation, can be obtained through satellites and radar, which can be used for drought monitoring. However, the accuracy is easily affected by detection instruments and terrain occlusion, and the ability to retrieve special shapes, such as solid precipitation, is limited. In addition, many meteorological monitoring stations on the ground can provide local raw meteorological observation data. The SPEI is a model to monitor, quantitatively analyze, and determine the spatiotemporal range of the occurrence of drought using meteorological observation data from various regions. It should supplement the result of drought monitoring with satellite and radar.
SPEI has two main characteristics: 1) it considers the deficits between precipitation and evapotranspiration comprehensively, that is, the balance of water; 2) multi-time scale characteristics. For 1) drought is caused by insufficient water resources. Precipitation can increase water, while evapotranspiration can reduce water. The differences between the two variables simultaneously and in space can characterize the balance of water. For 2), the deficits value of different usable water sources is distinct at different time scales due to the different evolution cycles of different types, resulting in various representations in temporal. By accumulating the difference between precipitation and evapotranspiration at different time scales, agricultural (soil moisture) droughts, hydrological (groundwater, streamflow, and reservoir) droughts, and other droughts can be distinguished by SPEI.
In our project, the dataset for SPEI calculation is ERA5-Land monthly averaged data from 1950 to the present. We selected years of data about partial areas of East Asia for experiments. Through the following flow of the SPEI calculation, we obtain the SPEI value for assessments of drought impact. The flow of the SPEI calculation is shown in Figure 39.
Figure 39 — Flow of the SPEI calculation.
WHU has provided the SPEI drought index calculation services through the OGC API — Processes, enabling interaction with other components. The current endpoint for OGC API — Processes is http://oge.whu.edu.cn/ogcapi/processes_api. This section will explain how to use this API for calculating the drought index.
Example:/processes http://oge.whu.edu.cn/ogcapi/processes_api/processes The API endpoint for retrieving the processes list.
Example:/processes/{processId} http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei The API endpoint for retrieving a process description (e.g. spei). This returns the description of “spei” process, which contains the inputs and outputs information.
Example:/processes/{processId}/execution http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/execution The API endpoint for executing the process. The spei process exclusively supports asynchronous execution, resulting in the creation of a job for processing. The request body:
{ “inputs”: { “startTime”: “2010-01-01”, “endTime”: “2020-01-01”, “timeScale”: 5, “extent”: { “bbox”: [73.95, 17.95, 135.05, 54.05], “crs”: “http://www.opengis.net/def/crs/OGC/1.3/CRS84” } } }
Example:/processes/{processId}/jobs/{jobId} http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/jobs/{jobId} The API endpoint for retrieving status of a job.
Example:/processes/{processId}/jobs/{jobId}/results http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/jobs/{jobId}/results The API endpoint for retrieving the results of a job, which are encoded as : [{ “value”: { “time”: “2000_02_01”, “url”: “http://oge.whu.edu.cn/api/oge-python/data/temp/9BC500C1B0E3438C090AF5C6F8602045/8d0357fb-8ffb-4e62-9c3a-55ad17a5831a/SPEI_2000_02_01.png” } }, …… ]
Figure 40 — The SPEI results for the date 2000_02_01.
4.4.3. Component: Data Cube Infrastructure
Outputs: Results in the form of GeoTIFF after processing in Data Cubes
What other component(s) can interact with the component: Any components requiring access to temperature and precipitation data, surface reflectance ARD, and drought risk map in part of Asia through OGC API
What OGC standards or formats does the component use and produce: OGC API- Coverages, OGC API — Processes
WHU has introduced GeoCube as a cube infrastructure for the management and large-scale analysis of multi-source data. GeoCube leverages the latest generation of OGC standard service interfaces, including OGC API-Coverages, OGC API-Features, and OGC API-Processes, to offer services encompassing data discovery, access, and processing of diverse data sources. The UML model of the GeoCube is given in Figure 5, and it has four dimensions: product, spatial, temporal, and band. Product dimension specifies the thematic axis for the geospatial data cube using the product name (e.g. ERA5_Precipitation or OSM_Water), type (e.g. raster, vector, or tabular), processes, and instrument name. For example, the product dimension can describe optical image products by recording information on the instrument and band. Spatial dimension specifies the spatial axis for the geospatial data cube using the grid code, grid type, city name, and province name. The cube uses a spatial grid for tiling to enable data readiness in a high-performance form. Temporal dimension specifies the temporal axis for the geospatial data using the phenomenon time and result time. Band dimension describes the band attribute of the raster products according to the band name, polarization mode that is reserved for SAR images, and product-level band. The product-level band is the information that is extracted from the original bands. For example, the Standardized Precipitation Evapotranspiration Index (SPEI) band is a product-level band that takes into account the hydrological process and evaluates the degree of drought by calculating the balance of precipitation and evaporation.
Figure 41 — The UML model of WHU Data Cube.
WHU has organized ERA5 temperature and precipitation data, surface reflectance ARD, and drought risk map into cubes and offers climate data services through the OGC API — Coverages, and OGC API — Processes. The API endpoint of Processes has given in the previous chapter. The API endpoint of Coverages is http://oge.whu.edu.cn/ogcapi/coverages_api, allowing users to query and retrieve the desired data from the cube. This section provides examples demonstrating how to access the data from the cube using OGC API — Coverages.
Example:/collections http://oge.whu.edu.cn/ogcapi/coverages_api/collections?bbox=112.65942,29.23223,115.06959,31.36234=10=2016-01-01T02:55:50Z/2018-01-01T02:55:50Z The API endpoint for querying datasets from the cube, and the query parameters including limit, bbox, and time.
Example:/collections/{collectionId} http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602 The API endpoint for retrieving the description of the coverage with the specified ID from the cube.
Example:/collections/{collectionId}/coverage http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage The API endpoint for retrieving the coverage in GeoTIFF format for the specified ID. Here is an example of the response:
Figure 42 — The coverage with the ID "2m_temperature_201602" in the Asian region.
Example:/collections/{collectionId}/coverage/rangetype http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage/rangetype The API endpoint for accessing the range type of the coverage, which is part of the band dimension members in the cube. In this example, the coverage consists of only one band dimension member.
Example:/collections/{collectionId}/coverage/domainset http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage/domainset The API endpoint for the domain set of the coverage, which is also the domain set of the cube.
4.5. Climate Resilience Data
4.5.1. Climate Projection Data
To make climate projection data more easily usable we transformed CMIP5 data (version 1 of our project), now working on CMIP6, into an Analysis Ready Data collection of indices of future temperature and precipitation. Climate summaries for the contiguous 48 states were derived from data generated for the 4th National Climate Assessment. These data were accessed from the Scenarios for the National Climate Assessment website. The 30-year mean values for 4 time periods (historic, early-, mid-, and late-century) and two climate scenarios (RCP 4.5 and 8.5) were derived from the Localized Constructed Analogs (LOCA) downscaled climate model ensembles, processed by the Technical Support Unit at NOAA’s National Center for Environmental Information.
Historical: 1976-2005
Early-Century: 2016-2045
Mid-Century: 2036-2065
Late-Century: 2070-2099
In order to display the full range of projections from individual climate models for each period, data originally obtained from USGS THREDDS servers were accessed via the Regional Climate Center’s Applied Climate Information System (ACIS). This webservice facilitated processing of the raw data values to obtain the climate hazard metrics available in CMRA.
As LOCA was only generated for the contiguous 48 states (and the District of Columbia), alternatives were used for Alaska and Hawaii. In Alaska, the Bias Corrected Spatially Downscaled (BCSD) method was used. Data were accessed from USGS THREDDS servers. The same variables provided for LOCA were calculated from BCSD ensemble means. However, only RCP 8.5 was available. Minimum, maximum, and mean values for county and census tracts were calculated in the same way as above. For Hawaii, statistics for two summary geographies were accessed from the U.S. Climate Resilience Toolkit’s Climate Explorer: Northern Islands (Honolulu County, Kauaʻi County) and Southern Islands (Maui County, Hawai’i County).
This data is being updated to CMIP6 and will be available in the latter half of 2023. The system is being expanded globally using NASA NEX CMIP6 data using the same time periods and climate scenarios.
4.5.2. Climate Indices
To provide a more approachable context to future climate, a collection of 47 indices of future temperature and precipitation are computed. These indices build upon prior work on Climdex indices and additional indices developed for National Climate Assessment 4 (NCA4).
Cooling Degree Days: Cooling degree days (annual cumulative number of degrees by which the daily average temperature is greater than 65°F) [degree days (degF)]
Consecutive Dry Days: Annual maximum number of consecutive dry days (days with total precipitation less than 0.01 inches)
Consecutive Dry Days Jan Jul Aug: Summer maximum number of consecutive dry days (days with total precipitation less than 0.01 inches in June, July, and August)
Consecutive Wet Days: Annual maximum number of consecutive wet days (days with total precipitation greater than or equal to 0.01 inches)
First Freeze Day: Date of the first fall freeze (annual first occurrence of a minimum temperature at or below 32degF in the fall)
Growing Degree Days: Growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F) [degree days (degF)]
Growing Degree Days Modified: Modified growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F; before calculating the daily average temperatures, daily maximum temperatures above 86°F and daily minimum temperatures below 50°F are set to those values) [degree days (degF)]
Growing-season: Length of the growing (frost-free) season (the number of days between the last occurrence of a minimum temperature at or below 32degF in the spring and the first occurrence of a minimum temperature at or below 32degF in the fall)
Growing Season 28F: Length of the growing season, 28°F threshold (the number of days between the last occurrence of a minimum temperature at or below 28°F in the spring and the first occurrence of a minimum temperature at or below 28°F in the fall)
Growing Season 41F: Length of the growing season, 41°F threshold (the number of days between the last occurrence of a minimum temperature at or below 41°F in the spring and the first occurrence of a minimum temperature at or below 41°F in the fall)
Heating Degree Days: Heating degree days (annual cumulative number of degrees by which the daily average temperature is less than 65°F) [degree days (degF)]
Last Freeze Day: Date of the last spring freeze (annual last occurrence of a minimum temperature at or below 32degF in the spring)
Precip Above 99th pctl: Annual total precipitation for all days exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]
Precip Annual Total: Annual total precipitation [inches]
Precip Days Above 99th pctl: Annual number of days with precipitation exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]
Precip 1in: Annual number of days with total precipitation greater than 1 inch
Precip 2in: Annual number of days with total precipitation greater than 2 inches
Precip 3in: Annual number of days with total precipitation greater than 3 inches
Precip 4in: Annual number of days with total precipitation greater than 4 inches
Precip Max 1 Day: Annual highest precipitation total for a single day [inches]
Precip Max 5 Day: Annual highest precipitation total over a 5-day period [inches]
Daily Avg Temperature: Daily average temperature [degF]
Daily Max Temperature: Daily maximum temperature [degF]
Temp Max Days Above 99th pctl: Annual number of days with maximum temperature greater than the 99th percentile, calculated with reference to 1976-2005
Temp Max Days Below 1st pctl: Annual number of days with maximum temperature lower than the 1st percentile, calculated with reference to 1976-2005
Days Above 100F: Annual number of days with a maximum temperature greater than 100degF
Days Above 105F: Annual number of days with a maximum temperature greater than 105degF
Days Above 110F: Annual number of days with a maximum temperature greater than 110degF
Days Above 115F: Annual number of days with a maximum temperature greater than 115degF
Temp Max 1 Day: Annual single highest maximum temperature [degF]
Days Above 32F: Annual number of icing days (days with a maximum temperature less than 32degF)
Temp Max 5 Day: Annual highest maximum temperature averaged over a 5-day period [degF]
Days Above 86F: Annual number of days with a maximum temperature greater than 86degF
Days Above 90F: Annual number of days with a maximum temperature greater than 90degF
Days Above 95F: Annual number of days with a maximum temperature greater than 95degF
Temp Min: Daily minimum temperature [degF]
Temp Min Days Above 75F: Annual number of days with a minimum temperature greater than 75degF
Temp Min Days Above 80F: Annual number of days with a minimum temperature greater than 80degF
Temp Min Days Above 85F: Annual number of days with a minimum temperature greater than 85degF
Temp Min Days Above 90F: Annual number of days with a minimum temperature greater than 90degF
Temp Min Days Above 99th pctl: Annual number of days with minimum temperature greater than the 99th percentile, calculated with reference to 1976-2005
Temp Min Days Below 1st pctl: Annual number of days with minimum temperature lower than the 1st percentile, calculated with reference to 1976-2005
Temp Min Days Below 28F: Annual number of days with a minimum temperature less than 28degF
Temp Min Max 5 Day: Annual highest minimum temperature averaged over a 5-day period [degF]
Temp Min 1 Day: Annual single lowest minimum temperature [degF]
Temp Min 32F: Annual number of frost days (days with a minimum temperature less than 32degF)
Temp Min 5 Day: Annual lowest minimum temperature averaged over a 5-day period [degF]
The individual web services of climate indices and raster data for download can be accessed at: https://resilience.climate.gov/pages/climate-model-content-gallery
Or for each scenario:
Historical: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-historical/about
RCP 4.5 Early Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-early-century/about
RCP 4.5 Mid Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-mid-century/explore?location=34.597533%2C-95.830000%2C5.00
RCP 4.5 Late Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-late-century/about
RCP 8.5 Early Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-early-century/about
RCP 8.5 Mid Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-mid-century/about
RCP 8.5 Late Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-late-century/explore?location=34.561983%2C-95.830000%2C5.00
The data can be viewed directly in the online map viewer or opened in ArcGIS Online, ArcGIS Desktop, or a StoryMap. To view in other softwares GeoService and KMZ URLs are on the right side of the page under View API Resources.
Figure 43 — View API Resources
4.5.3. Summarized Indices for Locations
To support easier interpretation and local decision making, the above indices were summarized by county, census tract, and tribal areas using the Zonal Statistics as Table utility in ArcGIS Pro. The results were joined into the corresponding geography polygons. A minimum, maximum, and mean value for each variable was calculated. This process was repeated for each time range and scenario. Precomputing enables quick map and graph response in the web application, and also provides as easily reusable download for someone who wants to utilize the data elsewhere.
To reuse the summarized services outside of the CRMA application or to download the processed data visit the links below for the geography of interest.
American Indian/Alaska Native/Native Hawaiian Areas: https://resilience.climate.gov/datasets/nationalclimate::climate-mapping-resilience-and-adaptation-cmra-climate-assessment-data/explore?layer=2=-0.000000%2C0.000000%2C2.71
On these pages, a list of buttons allow you to filter the selection to a subset by attribute or geography, download into a variety of formats, and translate the descriptive documentation for viewing in other languages.
4.5.4. Future Work
For ESRI’s contribution, the first version of CRMA was well received, it is widely used by the intended users and there is high interest by many others. Before the first version was released we had requests for other countries, and customizations of the project.
Due to many customization requests version 2 is being developed from inception with the intent for all code, from data processing python to web application Javascript to be available in Github repositories with documentation of typical customization workflows.
Use other climate projection data
Compute other indices
Summarize to other geographies
Customize the web application
The project is not only a solution, but a pattern for others to adapt to their data, geography, goals.
Version 2 data development is underway and will include more indices, both imperial and metric units, and min/max/mean for statistics instead of only areal mean. We will update all modeling to to CMIP6, and expand from US to global. Anticipated release in Q4 2023.
5. ARD to Decision Ready Indicator (DRI)
A decision Ready Indicator (DRI) is information and knowledge that is in such a format that it provides specific support for actions and decisions that have to be made. These indicators are pre-determined, using a set recipe which pulls together one or more ARDs to create an indicator of action and/or decision. DRIs hold significant importance as they serve as benchmarks or criteria to determine when a decision-making process is adequately prepared and can proceed efficiently. Their importance lies in several aspects. Firstly, DRIs facilitate efficient decision-making by signaling that all necessary information, analysis, and resources are available, minimizing delays and preventing hasty or uninformed decisions. Secondly, they ensure quality assurance by setting standards for the decision-making process, ensuring thorough consideration of relevant factors, accurate analysis, and reliable information. DRIs also promote accountability and transparency by defining expectations and providing a framework for evaluation, enabling stakeholders to understand the reasoning behind decisions and hold decision-makers accountable. Additionally, DRIs aid in effective resource allocation by identifying the point at which resources can be allocated, preventing wastage on underprepared decisions. They also assist in managing risks associated with decision-making by encouraging thorough analysis and consideration of potential risks. Furthermore, DRIs promote consistency and standardization, reducing subjectivity and increasing fairness across different decisions. In summary, DRIs play a crucial role in ensuring well-prepared, informed, and accountable decision-making processes, enhancing efficiency, quality, transparency, and resource management.
Analyze Ready Data (ARD), data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets, form the building blocks for DRIs. The transition from ARDs to DRIs encompasses a series of steps designed to extract meaningful insights and facilitate informed decision-making. It commences with the collection and preparation of data, where relevant information is gathered from diverse sources and formatted appropriately for analysis. This involves data cleaning, standardization, and transformation to ensure consistency and reliability. Following data preparation, the integration stage merges multiple data sources, aligning them based on common variables or identifiers, thereby creating a comprehensive dataset.
Subsequently, data exploration and analysis techniques are employed to delve into the dataset’s intricacies. Through statistical analysis, data visualization, and data mining, analysts uncover patterns, relationships, and trends that enable a deeper understanding of the underlying information. Feature engineering plays a crucial role in enhancing the analytical model’s performance. By selecting pertinent features, transforming existing variables, handling missing data, and encoding categorical variables, analysts optimize the model’s ability to extract insights from the data.
Once the data is prepared and features are engineered, model development ensues. Depending on the nature of the problem and the data at hand, analysts choose appropriate algorithms, such as regression, classification, clustering, or machine learning, to build predictive or analytical models. These models are then trained using a portion of the data, often referred to as the training set. Validation is performed using a separate portion of the data, the validation set, to assess the model’s performance and fine-tune it for optimal results.
With the validated model in place, the focus shifts to generating Decision Ready Indicators (DRIs). These indicators are specific metrics, scores, or predictions derived from the model’s outputs, providing actionable insights relevant to the decision-making process. The DRIs serve as valuable tools that support decision-makers in interpreting the analyzed data and guide them in making well-informed choices.
The generated DRIs become pivotal components in the decision-making process. Decision-makers leverage these indicators to assess different scenarios, evaluate risks, and identify opportunities. By incorporating the insights gained from the analyzed data and model outputs, decision-makers can make more informed and data-driven decisions, enhancing their ability to achieve desired outcomes.
It is worth noting that while the outlined steps provide a general framework, the specific implementation of the process may vary based on the unique context, data characteristics, and analytical techniques employed. Nonetheless, the overarching objective remains constant: to transform Analyze Ready Data into Decision Ready Indicators that facilitate effective decision-making. Below we provide examples on what DRIs can be developed in relation to Climate Resilience.
5.1. Wildfire hazard component
To develop its component, Intact migrated its previous proprietary wildfire hazard model to a private on-premise data science environment. For key inputs to the model, external connections to several open data repositories were established. To facilitate these access tests, several public open source datasets, such as climate model outputs, Earth observations, weather and geospatial, were vetted by the appropriate cybersecurity boards. The tests also informed experts of changes in platforms offerings, of new data products specifications, applicable licenses, and of current authoritative scientific references.
Figure 44 — Two samples of IFC’s current national wildfire hazard map
The table below shows the datasets accessed by Intact during the pilot.
5.1.1. Technical Interoperability Experiments (TIE) Table
| Dataset | Source | URL | Notes |
|---|---|---|---|
| National Fire Database fire polygon data | NRCan | https://cwfis.cfs.nrcan.gc.ca/datamart/download/nfdbpoly | Unable to establish SSL connection into private network |
| Fire Weather Index and its components | NRCan | https://cwfis.cfs.nrcan.gc.ca/downloads/fwi_obs/ | Unable to establish SSL connection into private network |
| Forest Fuels | NRCan | ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/cwfis/data/fuels/ | |
| Vegetation concentration and mass | NRCan | http://tree.pfc.forestry.ca/ | 503 Service Unavailable from private network |
| Daily reanalysis composites | NOAA | https://psl.noaa.gov/data/composites/day/ | |
| Monthly reanalysis composites | NOAA | https://psl.noaa.gov/cgi-bin/data/composites/printpage.pl | |
| Global temperature anomalies/trends | NASA | https://data.giss.nasa.gov/gistemp/maps/ | |
| Elevation at 30 meters | NASA | https://lpdaac.usgs.gov/products/nasadem_hgtv001/ | |
| Canadian Drought Monitor | AAFC | https://agriculture.canada.ca/atlas/data_donnees/canadianDroughtMonitor/data_donnees/shp/ | |
| Canadian Lightning Detection Network | NRCan | ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/CLDN/ | Connection timed out, can’t find alternate source |
| Topography | USGS | https://topotools.cr.usgs.gov/gmted_viewer/viewer.htm | Interactive map, not layers |
| Road segments | NRCan | ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/cwfis/data/base_data | Connection timed out, can’t find alternate source |
| Population of the world | Columbia U. | https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density/data-download | |
| CanVec Manmade Structures | NRCan | http://ftp.geogratis.gc.ca/pub/nrcan_rncan/vector/canvec/shp/ManMade/ | 503 Service Unavailable from private network |
Below is a summarized list of the key datasets required to produce or update a wildfire hazard map.
National fire database polygon data
Fire Weather Index (FWI) daily maps
Land cover maps
Drought conditions
Digital Elevation Model (DEM)
Population density
Fuel and vegetation data
Intact’s wildfire hazard map is developed exclusively for internal use. Aside from intellectual property terms, it is meant to be deployed in highly secured data environments, and as such it cannot readily interact with other components of the pilot at this point of time. The intent is to develop geospatial infrastructures and legal terms that would allow a closer collaboration with pilot’s participants.
Very early in the project, Intact also developed an H3 synthetic exposure dataset (see next figure) composed of 14M points spread out across the country in a statically representative pattern. The purpose of this dataset was to facilitate visualization and analysis of the exposure. It was also supposed to allow pilot participants to have a common exposure reference on which to develop decision-ready use cases for insurance, thus advancing towards standardization. Unfortunately, time constraints prevented update and sharing of this dataset.
Figure 45 — IFC’s exposure synthetic dataset, with Montreal – Ottawa corridor on the left, and close-up of Montreal on the right. Color scale represent relative risk density in each cell, while points are representative individual risks
5.2. The Blue Economy
Pelagis’ participation in the Climate Resilience pilot focuses on enhancing our view of a global oceans observation system by combining real-world ground observations with analysis ready datasets. Monitoring aspects of our oceans through both a temporal and spatial continuum while providing traceability through the observations process allows stakeholders to better understand the stressors affecting the health of our oceans and investigate opportunities to mitigate the longer term implications related to climate change.
The approach to address the needs for a sustainable ocean economy is to make Marine Spatial Planning a core foundation on which to build out vertical applications. Pelagis’ platform is based on a federated information model represented as a unified social graph. This provides a decentralized approach towards designing various data streams each represented by their well-known and/or standardized model. To date, service layers based on the OGC standards for Feature, Observations & Measurements, and Sensors APIs have been developed and extended for adoption within the marine domain model. Previous work provides for data discovery and processing of features based on the IHO S-100 standard (Marine Protected Areas, Marine Traffic Management, …); NOAA open data pipelines for major weather events (Hurricane Tracking, Ocean Drifters, Saildrones …); as well as connected observation systems as provided by IOOS and its Canadian variant, CIOOS.
5.2.1. From Raw Data to ARD and Decision Ready Indicators
The United Nations Framework Convention on Climate Change (UNFCCC) is supported through a number of organizations providing key observation data related to climate change. Of primary interest to this project scenario is the Global Climate Observing System (GCOS) and Global Ocean Observing System (GOOS), and the Joint Working Group on Climate (WG Climate) of the Committee on Earth Observation Satellites (CEOS). In-situ data sources are provided through a number of program initiatives sponsored through NOAA and provide key indicators for climate change that cannot be directly inferred from raw satellite information.
GCOS defines 54 Essential Climate Variables of which 18 ECVs apply to the oceans domain. Of these, only 6 ECVs may be inferred from satellite based earth observations while the remainder must be inferred through in-situ site observations and/or sampling programs.
The following table identifies the ocean-specific ECVs and associated providers.
Table 3
| Variable | Description | Source of Indicator |
|---|---|---|
| Ocean Colour | Provides indictation of phytoplankton based on Ocean Colour Radiance (OCR) | ESA CEOS |
| Carbon Dioxide Partial Pressure | Primary indicator of the exchange of CO2 at the ocean surface | NOAA |
| Ocean Acidity | pH of ocean water as measured at varying depths and locations | NOAA PMEL |
| Phytoplankton | Indicator of the health of the ecosystem associated with the food web and directly a result of increased CO2 and eutrophication | NOAA |
| Sea Ice | Sea ice coverage associated with the ocean surface and a concern reflected in warming surface temperatures and sea level rise | |
| Sea Level | Sea level global mean and variability leading to sea level rise | |
| Sea State | Wave height, direction, wavelength as indicators of energy at the ocean surface | |
| Sea-surface Salinity | The proportion of ocean water comprised of salt and indicator of mortality rates in shellfish | |
| Sea-surface Temperature | Directly affects major weather patterns and ecosystems | ESA CEOS; NOAA Monitoring Stations; NOAA Saildrone program |
| Surface Current | Transports heat, salt and passive tracers and has a large impact on seaborne commerce and fishing |
As well, social and economic key indicators related to the area of interest are ingested to identify relationships between the immediate effects of climate change on the associated human activity.
Table 4
| Variable | Description | Source of Indicator |
|---|---|---|
| AQ Landings | Annual yields associated with Aquaculture sites within a region of interest | MaineAQ |
| GDP | Gross Domestic Product ($USD) associated with dependent human activities within the region of interest | US Census |
| Employment | Number of individuals dependent on the targeted ecosystem | US Census |
| Population | Number of people inhabiting the area of interest associated with the ecosystem | US Census |
5.2.2. Approach
Each ECV applicable to the use case is resolved as a service endpoint representing the area of interest, associated samplings and observations, and where possible inferred from earth observation datasets transformed as ‘analysis ready’. Earth observation datasets are sourced through the ESA GCOS service endpoint; Ocean related samplings and in-situ observations are sourced through NOAA; socio-economic data is sourced from various open data portals available through government agencies.
The project effort centers around 3 key challenges * the ability to collect data relevant to Climate Resilience; * the ability to apply the data in a coherent and standardized manner in which to draw out context; * and the ability to impart insight to community members and stakeholders so as to identify, anticipate and mitigate the effects of climate change across both local and international boundaries.
Each of these activities aligns with the best practices and standards of the OGC and are used as input to the MarineDWG MSDI reference model.
Figure 46 — Architecture
5.3. DRI: Heat Impact and Drought Impact Components
5.3.1. Heat Impact DRI Component
This component takes the climate scenario summary ARD results from the ARD component and analyzes them to derive estimated heat impacts over time, based on selected climate scenarios. Central to this is the identification of key heat impact indicators required by decision makers and the business rules needed to drive them. Process steps include data aggregation and statistical analysis of maximum temperature spikes, taking into account the cumulative impacts of multiple high temperature days. Heat exhaustion effects are likely dependent on duration of heat spells, in addition to high maximum temperatures on certain days.
Figure 47 — ARD Query: Monthly Max Temp Contours
Figure 48 — ARD Query: Max Mean Monthly Temp > 25C
Figure 49 — Town of Lytton - location where entire town was devastated by fire during the heat wave of July 2021 - same location highlighted in ARD query from heat risk query in previous figure
5.3.2. Drought Impact DRI Component
This component takes the climate scenario summary ARD results from the ARD component and analyzes them to derive estimated drought risk impacts over time based on selected climate scenarios. It also feeds drought related environmental factors to other pilot DRI components for more refined drought risk analysis. For the purposes of this pilot, it was recognised that more complex indicators such as drought are likely driven by multiple environmental and physical factors. As such, our initial goal was to select and provide primary climate variable data that would be useful for deriving drought risks in combination with other inputs. Given that the primary input to drought models is precipitation, or lack thereof, we developed a data flow that extracted total precipitation per month and made this available both as a time series CSV and GeoJSON datasets, as well as OGC API features time series points. This climate scenario primary drought data was provided for the province of Manitoba and for Los Angelas. These two regions were chosen since we had pilot participants interested in each of these regions and in the case of Manitoba there is also a tie in to future work as this is an area of interest for the subsequent Disaster Pilot 2023.
For the LA use case, we worked with Laubwerk to provide them with climate change impact data that could help drive a drought impact that could affect their future landscape visualization model. The idea is that based on changes to climatic variables, certain areas may be more or less suited to different vegetation types, causing the distribution of vegetation to change over time. For more on their component, please refer to section 7: From Data To Visualization.
In the case of this visualization component, simply providing precipitation totals per month were not sufficient to drive the needs of their vegetation model. In this case we did not have an intermediate drought model to feed climate variables to. In the absence of a more comprehensive drought model, we decided to develop a proxy drought risk indicator by normalizing the difference between future precipitation and past.
Calculations were made using the difference between time series grids of projected precipitation and historical grids of mean precipitation per month. These precipitation deltas were then divided by the historical max — mean per month to derive a precipitation index. The goal was to provide a value between -1 and +1 where 1 = 100% of past mean precipitation for that month. Naturally this approach can generate values that exceed the range of -1 to 1 if the projected precipitation values exceed the historic max or min. The goal was not so much to predict future absolute precipitation values but rather generate an estimated for precipitation trends given the influence of climate change. For example, this approach can help answer the question — in 30 years for a given location, compare to historical norms, by what percentage do we expect precipitation to increase or decrease. Laubwerk can then take these results and decide what degree of drought stress will cause a specific vegetation species to die out for a particular location.
Interesting patterns emerged for the LA area that we ran this process on deltas between projected and historical precipitation. While summers are typically dry and winters are wet and prone to flash floods. Initial data exploration seemed to show an increase in drought patterns in the spring and fall. More analysis needs to be done to see if this is a general pattern or simply one that emerged from the climate scenario we ran. However, this is the type of trend that local planners and managers may benefit from having the ability to explore once they have better access to climate model scenario outputs along with the ability to query and analyze them.
Figure 50 — FME Query Workflow: Geopackage precipitation delta time series to GeoJSON points
Figure 51 — FME Query Parameters: Geopackage precipitation delta time series to GeoJSON points
Figure 52 — FME Data Inspector: precipitation delta result showing potential drought risk for areas and times with significantly less precipitation than past
This approach is only a start and just scratches the surface in terms of what is possible for future drought projection based on climate model scenario ECVs. The specific business rules used to assess drought risk are still under development. FME provides a flexible data and business rule modeling framework. This means that as indicators and drought threshold rules are refined, it’s relatively straightforward to adjust the business rules in this component to refine our risk projections. Also, business rule parameters can be externalized as execution parameters so that end users can control key aspects of the scenario drought risk assessment without having to modify the published FME workflow. However one of the main goals of this pilot was not so much to produced highly refined forecast models for drought but rather to demonstrate the data value chain whereby raw climate model data cube outputs can feed a data pipeline that filters, refines, simplifies the data and ultimately can be used to drive indicators that help planners model visualize and understand the effects of climate change on the landscapes and environments within their communities.
To support future drought risk estimates for Manitoba, we also provided a precipitation forecast time series to Pixalytics as an input to their drought analytics and DRI component. Their component provides a much more sophisticated indicator of drought probability since in addition to precipitation it also takes into account soil moisture and vegetation. The goal was to extract precipitation totals per time step from the downscaled RCM — regional climate model ECV outputs for Manitoba based on CMIP5 (Coupled Model Intercomparison Project Phase 5) model results obtained from Environment Canada. For this use case the grids have a spatial resolution of roughly 10km and a temporal resolution a monthly time step. Pixalytics then ran their drought model based on these precipitation estimates in order to asses potential future drought risk in southern Manitoba. The data was provided to Pixalytics initially as a GeoJSON feed of 2d points derived from the data cube cells with precipitation totals per cell. We later also provided this same data feed as a OGC API Feature service.
For future phases of the climate or disaster pilots, it may be useful to explore additional approaches for both precipitation data analysis and combination with other related datasets and external models. It may be useful to segment cumulative rainfall below a certain threshold Pt within a certain time window (days, weeks or months), since cumulative rainfall over time will be crucial for computing water budgets by watershed or catch basin. To do this we would like to test the use of a higher resolution time step such as daily, to see if the increased resolution reveals patterns of interest that the coarser monthly time step does not. There are also other statistical RCM results that might be useful to make available (mean, min, max). Besides precipitation, climate models also generate soil moisture predictions which could used by this component to assess drought risk. This component would also benefit from integration with topography, DEMs and hydrology related data such as river networks, water bodies, aquifers and watershed boundaries. Therefore rather than just computing precipitation deltas at the cell level, it would likely be useful to sum precipitation by catch basin and compute future trends that may indicate potential drought or flood.
The specific business rules used to assess drought risk are still under development. FME provides a flexible data and business rule modeling framework. This means that as indicators and drought threshold rules are refined, it’s relatively straightforward to adjust the business rules in this component to refine our risk projections. Also, business rule parameters can be externalized as execution parameters so that end users can control key aspects of the scenario drought risk assessment without having to modify the published FME workflow.
It should be stressed that the field of drought modelling is not new and there are many drought modelling tools available that are far more sophisticated than anything described above. As such, subsequent Climate and Disaster pilots should explore how future climate projections can be funneled into these more mature climate models in an automated fashion to produce more refined estimates of projected drought risk. That said, we need to start somewhere, and it is hoped that this basic demonstration of the raw data to ARD to DRI value chain for drought can provide some insights into what type of indicators we may want to generate to help better understand future drought risks, and where we may want to improve on this process.
6. From Data to Visualization
Advances in data representation and visualization have revolutionized the way we understand and analyze information. The ability to transform raw data into meaningful visual representations has become increasingly important across various fields, including climate change. The exponential growth of data generated by various sources such as in-situ sensors, EO sensors, and social media has all led to the emergence of big data. Data visualization techniques help in extracting insights, identifying patterns, and making data-driven decisions in the face of vast and complex datasets. Visualization plays a crucial role in exploring, summarizing, and communicating the results of data analysis, making it easier for decision-makers to comprehend complex information. Data visualization enhances storytelling by presenting information in a visually engaging and intuitive manner. It helps convey complex ideas more effectively, enabling clearer communication of data-driven narratives to both technical and non-technical audiences.
Above all, general need for data visualization arises from the complexity and volume of data that is involved with climate change adaptation. Data visualizations are stimulated by the desire for actionable insights, and the importance of clear communication in various domains.
Below we provide some examples of how big data can be visualized in such a way that it captures the impact of climate change on for example vegetation in urban areas, or the impact of of climate change on climate hazards. And how to overcome challenges to realize these visualizations.
6.1. Visualizing the Impact of Climate Change and Mitigation on Vegetation
One of the biggest challenges in communicating climate change is to tie global changes to the local impact they will have. Photorealistic visualization is a critical component for assessing and communicating the impact of environmental changes, and possibilities for mitigation. For this to work, it is crucial for visualizations to reflect the underlying data accurately and allow for quick iteration. In this regard, manual visualization processes are inferior. As much as possible, visualizations of real-life scenarios should be driven directly by available data of present states and simulations of possible scenarios. Our contribution is a first attempt at doing just that, determining what already works and what doesn’t with existing data and technology.
As our contribution to the Climate Resilience Pilot we explored such data-driven high-quality visualizations, focusing on the impact on vegetation. Due to the nature of this being a pilot, we constrained ourselves in terms of coverage area, to account for limited time and to cope with potentially limited data availability. This ensured that we were able to make the full connection from input data to final visualization, drawing valuable conclusions for broader application in the future. This size limitation will allow us to produce meaningful results if data transfer and processing is slow or even if it must be processed in manual or half-automated ways due to inconsistent formatting. It also lets us visualize a high level of detail without having to account too much for the sheer amount of data we could face with very large areas.
We selected a relatively small section of Los Angeles for actual visualization. The rationale behind this choice of location had several components:
The given area that will (and already does) see considerable direct impact of climate change through heat, drought, wildfires, etc.
It contains different areas of land use (from deeply urban and sub-urban to unmanaged areas).
Since it is part of a major metro area, the results will be relevant to a large population base
Some known mitigation measures that can be considered for visualization are in place.
Other external (non climate change) known influences on vegetation, such as pests, irrigation limitations, known life spans of relevant plant species, etc.) are in play that could be considered.
6.1.1. Source Data
Our visualization ties data that is very global together with data that is hyper-local. That means we need to draw on data from a wide variety of sources that are not usually combined. Examples of data sources used for our visualization are:
Satellite Imagery
Building Footprints and Heights
Plant Inventory from Bureau of Street Services and Department of Recreation and Parks
Results from Climate Models, particularly RPC 4.5 data that was pre-processed for this purpuse by Safe Software as part of their work for this pilot (see the Safe Software ARD component in this document for more details)
3D Plant Models from the Laubwerk database
Plant Metadata to Judge Climate Change Impact on Specific Species through given Environmental factors, also from the Laubwerk database
Information on local mitigation measures from various sources
6.1.2. Results
The aforementioned data sources were combined to create a detailed visualization of the area in question. The pairs of images below show a visualization of the status quo as first image and then a composite of the four scenarios we visualized (one scenario per vertical stripe). The scenarios are projections of possible climate scenarios with and without mitigation measures in place and are in the following order:
Year 2045 without any mitigation measures. Plants that were likely to die off due to adverse climate events were just removed based on a probability.
Year 2070 without any mitigation measures with plants removed like in the scenario before.
Year 2045 with mitigation measures. Plants that were just removed in the two scenarios before have been replaced by plants that are more resilient and are part of the aforementioned initiatives for better climate resilience.
Year 2070 with mitigation measures with the same replacement logic as in the scenario before.
It should be stressed that these are visualizations of possible outcomes, but there are are many factors that make hard to make exact predictions! This contribution is merely meant as an example of how data could be used to drive scenario-based hyper-local visualization.
Figure 53 — Overview of the Visualized Region (Status Quo)
Figure 54 — Overview of the Visualized Region (Climate Projection With and Without Mitigation Scenarios as Described at the Start of the Results Section
Figure 55 — Above the Corner Sunset Blvd and N Curson Ave Looking North-East (Status Quo)
Figure 56 — Above the Corner Sunset Blvd and N Curson Ave Looking North-East (Climate Projection With and Without Mitigation Scenarios as Described at the Start of the Results Section)
Figure 57 — Corner Franklin Ave And N Sierra Bonita Ave Looking East (Status Quo)
Figure 58 — Corner Franklin Ave And N Sierra Bonita Ave Looking East (Climate Projection With and Without Mitigation Scenarios as Described at the Start of the Results Section)
Figure 59 — Corner Hollywood Blvd And Camino Palmero St Looking Looking North (Status Quo)
Figure 60 — Corner Hollywood Blvd And Camino Palmero St Looking Looking North (Climate Projection With and Without Mitigation Scenarios as Described at the Start of the Results Section)
6.1.3. Challenges and Learnings
The goal of a visualization like we did is to make data and its implications visible on a hyper-local level. The hope behind this is to turn a large amount of abstract data into something the general public can better judge the very local impact of global changes.
This hyper-locality brings to light a number of problems with the granularity, availability, and machine readability of existing data. Relating to our specific inputs, this means:
Producing a high fidelity photorealistic 3D model of a specific area is still not easy. Even in an urban area of an industrialized country like we picked (which usually have better data availability), we had to resort to relatively simple elevation data and building footprints. There are solutions for this on the horizon, but general availability is not a given, yet. 3D models based on photogrammetry seem like a promising approach to reach higher fidelity where available, but that generally available datasets like these currently lack classification, so we would not be able to remove and replace vegetation elements. This will probably improve and become more widely available in the near future.
Information about existing vegetation is of varying quality and completeness. Detailed data is sometimes maintained by different authorities with different scopes. In our case we used data from the Bureau of Street Services as well as the Department of Recreation and Parks. Those datasets have different data layout, different depth and quality of data. OpenStreetMap also sometimes has vegetation data, but coverage and data quality is also problematic. None of the aforementioned really cover individual plants on private property or unmanaged land, which we had to fill in from photogrammetry, satellite imagery, and aerial photography.
Climate projection data is pretty widely available and generally easy to process in terms of data volume, because the areas a visualization will typically cover is pretty small compared to the resolution of most climate models. What is still a challenge is to turn climate scenario data into properties that are needed to easily model the impact on vegetation, like the probability of extreme drought, heat, or fire events. This was partially addressed by other contributions to this pilot and we expect it to see further improvements.
Exact data on average plant behavior in the context of relevant climate indicators is extremely patchy. Most data is only qualitatively in nature. Data gathering is complex because of the large number of factors at play when judging health of plants. This is a complex researach topic that will need more work, both to produce more reliable projections based on existing research, but also on how to gather data about or predict plant health more reliably on a large scale.
Information about climate change mitigation is often not present in a machine readable format. In our specific case, we gathered information manually from publicly available material, mostly websites. Part of the problem here is that several stakeholders are working on mitigation measures, from different local government organizations, over non-profit organizations, to private companies. Examples relevant to our specific example are City Plants (a non-profit supported by Los Angeles Department of Water and Power) and the County of Los Angeles Parkway Trees Program. This manual way of data gathering obviously will not scale, is prone to data being missed, and has no unified format. All of this makes automated processing next to impossible at the moment.
There may be further factors that need to be considered, which are not part of any of the existing data sources. In this specific case we have the pretty high average age and also various pests and diseases that the Mexican fan palm (Washingtonia robusta), which has become such a distinctive feature of Southern California, especially Los Angeles, is suffering from. While this isn’t directly related to climate change, it still needs to be considered for any visualization to be accurate.
As was expected, the data-driven visualization of very local phenomena and changes is a challenging problem which surfaces lots of issues in terms of data availability as well as standardization and compatibility of storage formats.
6.2. 5D Meta World
Presagis offered the V5D rapid 3D (trial) Digital Twin generation capability to Laubwerk Presagis gathered open source GIS dataset for the Hollywood region in order to match the location of the tree dataset from Laubwerk Using V5D, Presagis created a representative 3D digital twin of the building and terrain. Presagis imported Laubwerk tree point dataset providing vegetation type information inside V5 Presagis provided V5D Unreal plugin to Laubwerk in order to allow the insertion of the Laubwerk 3D tree (as Unreal assets) into the scene. Using V5D, Laubwerk is capable of adapting the tree model in order to demonstrate the impact of climate change on the city vegetation
Presagis also provided to Laubwerk its V5D AI extracted vegetation dataset in order to complement the existing tree dataset as needed.
Figure 61 — image of the Presagis deliverable to Laubwerk. At this stage, all trees are using the same 3D model (palm tree). Laubwerk will use V5D to assign a representative 3D model based the on point feature attribution accessible in V5D. With V5D, this operation takes seconds to do and visualize the result in 3D.
6.3. CRMA Web Application
Decision makers, public authorities, and citizens will primarily access data via a custom ESRI web application, providing a simple dashboard interface for viewing interactive maps and graphs of the indices, and output formatted reports. The indices are grouped by 5 climate hazard types (Wildfire, Heat, Drought, Inland Flooding, Coastal Inundation). The current US project (https://livingatlas.arcgis.com/assessment-tool/explore/details) can be explored to gain context of what the global project will be.
Figure 62 — Climate Mapping For Resilience and Adaptation (CMRA) portal, US project view
Figure 63 — Climate Mapping For Resilience and Adaptation (CMRA) portal, showing number of max temperature for the period 2023-2064
The application also outputs formatted reports by county or census tract summarizing the data in a format easy to share with others.
Figure 64 — Application output reports
For each of those 5 climate hazards there is a corresponding StoryMap to further explain that hazard type, visualize the current and future hazard, and provide links to additional relevant resources.
Extreme Heat: https://storymaps.arcgis.com/stories/5e482f11d2514191bb89c20638d98b3c
Drought: https://storymaps.arcgis.com/stories/634ee231bb6743b88d23bda96fb838e9
Wildfire: https://storymaps.arcgis.com/stories/ae2a8072429643f395f8f509df955ae6
Flooding: https://storymaps.arcgis.com/stories/4ea811276aa641018f3a8d4e28585244
Coastal Inundation: https://storymaps.arcgis.com/stories/f3ce292c0211400699b6e36985e561a6
6.4. NOAA’s Environmental Data Retrieval API
For the D100 Client Instance deliverable, Ecere enhanced its GNOSIS Cartographer geospatial client to better support visualizing and accessing multi-dimensional datasets, both from local sources and remote sources such as through OGC API standards. Support for the OGC API — Environmental Data Retrieval (EDR) standard as well as for OGC netCDF was implemented in the GNOSIS Software Development Kit. The GNOSIS implementation of the GNOSIS Map Tiles specification was also enhanced as an efficient format to store and exchange n-dimensional coverage tiles, including support for multiple pressure levels within a single tile packet. A pressure level selector control was added to the user interface, as seen below.
Figure 65 — Ecere’s GNOSIS Cartographer client accessing 4-dimensional CMIP5 air temperature dataset from GNOSIS Map Server, showing pressure level selector
Figure 66 — Ecere’s GNOSIS Cartographer client accessing 4-dimensional ERA5 relative humidity dataset from GNOSIS Map Server, showing pressure level selector
Technology Integration Experiments were performed with NOAA’s experimental EDR API deployment, providing feedback to its developers to help achieve conformance to the Standard, as well as help improving interoperability and usability. The results of visualization experiments with multiple data collections are shown below.
Figure 67 — Ecere’s GNOSIS Cartographer client accessing NOAA’s EDR API (nclimgrid-monthly collection, minimum daily temperature for January 2014)
Figure 68 — Ecere’s GNOSIS Cartographer client accessing NOAA’s EDR API (nclimgrid-monthly collection, minimum daily temperature for January 2022)
Figure 69 — Ecere’s GNOSIS Cartographer client accessing NOAA’s EDR API (nclimgrid-monthly collection, maximum daily temperature for January 2014)
Figure 70 — Ecere’s GNOSIS Cartographer client accessing NOAA’s EDR API (nclimgrid-monthly collection, maximum daily temperature for January 2022)
Figure 71 — Ecere’s GNOSIS Cartographer client accessing NOAA’s EDR API (nclimgrid-monthly collection, precipitations for January 2014)